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Unit 1: Introduction to Autonomous Robotics
Computer Science 4766/6778
Department of Computer ScienceMemorial University of Newfoundland
January 12, 2009
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 1 / 25
1 IntroductionWhat is Autonomous RoboticsWhat is this Course About?Relationship to Other Disciplines
2 Major ParadigmsThe Model-Based ParadigmBehaviour-Based RoboticsProbabilistic Robotics
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 2 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?
Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willed
Something which is autonomous operates independently of externalcontrols
Robots?
Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?
Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?
Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
What is Autonomous Robotics
Autonomous?
Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols
Robots?Comes from the Czech robotnik, meaning ‘workman’
From Karl Capek’s play “Rossum’s Universal Robots”
“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]
“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 3 / 25
Which one of these robots is more autonomous?
Autonomous robotics is distinct from industrial robotics which isconcerned with the operation of robots in highly controlled environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 4 / 25
Which one of these robots is more autonomous?
Autonomous robotics is distinct from industrial robotics which isconcerned with the operation of robots in highly controlled environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 4 / 25
Which one of these robots is more autonomous?
Autonomous robotics is distinct from industrial robotics which isconcerned with the operation of robots in highly controlled environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 4 / 25
Which one of these robots is more autonomous?
The Mars Pathfinder rover Sojourner (1997) was primarily manuallydriven; The Mars Exploration rovers Oppourtunity and Spirit (2003 -present) exhibit limited (but increasing) autonomy
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 5 / 25
Which one of these robots is more autonomous?
The Mars Pathfinder rover Sojourner (1997) was primarily manuallydriven; The Mars Exploration rovers Oppourtunity and Spirit (2003 -present) exhibit limited (but increasing) autonomy
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 5 / 25
Which one of these robots is more autonomous?
The Mars Pathfinder rover Sojourner (1997) was primarily manuallydriven
; The Mars Exploration rovers Oppourtunity and Spirit (2003 -present) exhibit limited (but increasing) autonomy
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 5 / 25
Which one of these robots is more autonomous?
The Mars Pathfinder rover Sojourner (1997) was primarily manuallydriven; The Mars Exploration rovers Oppourtunity and Spirit (2003 -present) exhibit limited (but increasing) autonomy
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 5 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheels
The dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)
Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the world
Maintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of position
Navigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
What is this Course About?
This course provides an introduction to the computational aspects ofautonomous mobile robotics
We will not consider the following in any detail:
The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion
i.e. How forces on a robot’s body lead to velocities
We will focus on how to program a robot to...
Move in a particular direction (kinematics & control)Interpret sensor data and infer information about the worldMaintain an estimate of positionNavigate through both known and unknown environments
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 6 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)
Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)
Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational Geometry
Algorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal Processing
Control Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for robotics
Robotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
Relationship to Other Disciplines
Computer Science
Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms
Computer and Electrical Engineering
Signal ProcessingControl Systems
Mechanical Engineering
Psychology, Neuroscience, Biology
Biological insights for roboticsRobotic instantiations of models
Autonomous Robotics (AR) is an inherently interdisciplinary field
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 7 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledgeRecognition of placesEstimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledgeRecognition of placesEstimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizon
Requires:
Incremental acquisition of knowledgeRecognition of placesEstimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledgeRecognition of placesEstimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledge
Recognition of placesEstimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledgeRecognition of places
Estimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledgeRecognition of placesEstimation of position
Real-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
AR as a Distinct Field of Study
AR is not just an application area for the preceding disciplines
By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]
Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:
Incremental acquisition of knowledgeRecognition of placesEstimation of positionReal-time response
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 8 / 25
Major Paradigms
A few major paradigms in AR have emerged
Model-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
Major Paradigms
A few major paradigms in AR have emergedModel-Based Paradigm
Build and maintain a model of the world and use it for planning
Behaviour-Based Robotics
Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours
Probabilistic Robotics
Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions
This list is not exhaustive
The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 9 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPS
www.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPS
www.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPS
www.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPS
www.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPS
www.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPSwww.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPSwww.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
The Model-Based Paradigm
The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI
e.g. Nilsson and others atSRI developed “Shakey”
Shakey operated in anenvironment speciallymodified to assist its visionsystem
Its task was to pushparticular objects from oneone place to another
Based on STRIPSwww.ai.sri.com/shakey
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 10 / 25
STRIPS (STanford Research Institute Problem Solver)
Best illustrated using a “blocks world” environment
[Luger and Stubblefield, 1998]
Environmental state described by a set of predicatesontable(a) on(b,a) clear(b)ontable(c) on(e,d) clear(c)ontable(d) gripping() clear(e)
Operations in the world represented by operations on these predicates:pickup(X), putdown(X), stack(X,Y), unstack(X,Y)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 11 / 25
STRIPS (STanford Research Institute Problem Solver)
Best illustrated using a “blocks world” environment
[Luger and Stubblefield, 1998]
Environmental state described by a set of predicatesontable(a) on(b,a) clear(b)ontable(c) on(e,d) clear(c)ontable(d) gripping() clear(e)
Operations in the world represented by operations on these predicates:pickup(X), putdown(X), stack(X,Y), unstack(X,Y)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 11 / 25
STRIPS (STanford Research Institute Problem Solver)
Best illustrated using a “blocks world” environment[Luger and Stubblefield, 1998]
Environmental state described by a set of predicatesontable(a) on(b,a) clear(b)ontable(c) on(e,d) clear(c)ontable(d) gripping() clear(e)
Operations in the world represented by operations on these predicates:pickup(X), putdown(X), stack(X,Y), unstack(X,Y)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 11 / 25
STRIPS (STanford Research Institute Problem Solver)
Best illustrated using a “blocks world” environment[Luger and Stubblefield, 1998]
Environmental state described by a set of predicatesontable(a) on(b,a) clear(b)ontable(c) on(e,d) clear(c)ontable(d) gripping() clear(e)
Operations in the world represented by operations on these predicates:pickup(X), putdown(X), stack(X,Y), unstack(X,Y)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 11 / 25
STRIPS (STanford Research Institute Problem Solver)
Best illustrated using a “blocks world” environment[Luger and Stubblefield, 1998]
Environmental state described by a set of predicatesontable(a) on(b,a) clear(b)ontable(c) on(e,d) clear(c)ontable(d) gripping() clear(e)
Operations in the world represented by operations on these predicates:pickup(X), putdown(X), stack(X,Y), unstack(X,Y)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 11 / 25
An operation such aspickup(X) affects the statedescription set as follows:
if gripping() ∧ clear(X) ∧ ontable(X)
add: gripping(X)
delete: ontable(X), gripping()
(This operation is for picking up objects lying
directly on the table)
The state space is searchedfor the goal state
STRIPS implements asearch through state spaceto find a sequence ofoperations that wouldtransform the initial stateinto the goal state
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 12 / 25
An operation such aspickup(X) affects the statedescription set as follows:
if gripping() ∧ clear(X) ∧ ontable(X)
add: gripping(X)
delete: ontable(X), gripping()
(This operation is for picking up objects lying
directly on the table)
The state space is searchedfor the goal state
STRIPS implements asearch through state spaceto find a sequence ofoperations that wouldtransform the initial stateinto the goal state
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 12 / 25
An operation such aspickup(X) affects the statedescription set as follows:
if gripping() ∧ clear(X) ∧ ontable(X)
add: gripping(X)
delete: ontable(X), gripping()
(This operation is for picking up objects lying
directly on the table)
The state space is searchedfor the goal state
STRIPS implements asearch through state spaceto find a sequence ofoperations that wouldtransform the initial stateinto the goal state
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 12 / 25
An operation such aspickup(X) affects the statedescription set as follows:
if gripping() ∧ clear(X) ∧ ontable(X)
add: gripping(X)
delete: ontable(X), gripping()
(This operation is for picking up objects lying
directly on the table)
The state space is searchedfor the goal state
STRIPS implements asearch through state spaceto find a sequence ofoperations that wouldtransform the initial stateinto the goal state
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 12 / 25
An operation such aspickup(X) affects the statedescription set as follows:
if gripping() ∧ clear(X) ∧ ontable(X)
add: gripping(X)
delete: ontable(X), gripping()
(This operation is for picking up objects lying
directly on the table)
The state space is searchedfor the goal state
STRIPS implements asearch through state spaceto find a sequence ofoperations that wouldtransform the initial stateinto the goal state
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 12 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult
Sensor data is noisy and ambiguousUpdating the model is expensive and error-proneWorld / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]
The modelling process is difficult
Sensor data is noisy and ambiguousUpdating the model is expensive and error-proneWorld / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult
Sensor data is noisy and ambiguousUpdating the model is expensive and error-proneWorld / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult
Sensor data is noisy and ambiguous
Updating the model is expensive and error-proneWorld / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult
Sensor data is noisy and ambiguousUpdating the model is expensive and error-prone
World / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult
Sensor data is noisy and ambiguousUpdating the model is expensive and error-proneWorld / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Deficiencies
A number of deficiencies of the model-based paradigm have beenidentified
The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult
Sensor data is noisy and ambiguousUpdating the model is expensive and error-proneWorld / model deviations render plans useless
Many of these deficiencies remain in current work; some may beintractable
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 13 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)
W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)
Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak light
Back away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright light
Avoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviour
Above the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weak
Thus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Behaviour-Based Robotics (BBR) [Arkin, 1998]
Term coined in the 1980’s but roots stretch back much further
Cybernetics: The science of control and communications in bothanimal and machine
Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)
Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)
Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 14 / 25
Braitenberg Vehicles [Arkin, 1998]
Valentino Braitenberg devised thought experiments to illustrate thatcomplex behaviour could result from very simple mechanisms (1984)
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layersNo possibility for parallelismOverall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layersNo possibility for parallelismOverall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layersNo possibility for parallelismOverall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layers
No possibility for parallelismOverall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layersNo possibility for parallelism
Overall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layersNo possibility for parallelismOverall update cycle is slow
The introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The Subsumption Architecture [Brooks, 1986]
Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]
Brooks criticized the model-based functional decomposition
The tight coupling between layers leads to problems:
Errors made by earlier layers propagate to subsequent layersNo possibility for parallelismOverall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 17 / 25
The subsumption architecture organizes behaviours into vertical layerswith each layer acting out its own behaviour independently
There is no central controller; Each layer processes sensor data andcontrols actuators unless...
...suppressed or inhibited by another layerThus, there is a dynamic hierarchy of layers
New behaviours implemented as new layers without modifyingexisting layers (evolutionary growth)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 18 / 25
The subsumption architecture organizes behaviours into vertical layerswith each layer acting out its own behaviour independently
There is no central controller; Each layer processes sensor data andcontrols actuators unless...
...suppressed or inhibited by another layerThus, there is a dynamic hierarchy of layers
New behaviours implemented as new layers without modifyingexisting layers (evolutionary growth)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 18 / 25
The subsumption architecture organizes behaviours into vertical layerswith each layer acting out its own behaviour independently
There is no central controller; Each layer processes sensor data andcontrols actuators unless...
...suppressed or inhibited by another layer
Thus, there is a dynamic hierarchy of layers
New behaviours implemented as new layers without modifyingexisting layers (evolutionary growth)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 18 / 25
The subsumption architecture organizes behaviours into vertical layerswith each layer acting out its own behaviour independently
There is no central controller; Each layer processes sensor data andcontrols actuators unless...
...suppressed or inhibited by another layerThus, there is a dynamic hierarchy of layers
New behaviours implemented as new layers without modifyingexisting layers (evolutionary growth)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 18 / 25
The subsumption architecture organizes behaviours into vertical layerswith each layer acting out its own behaviour independently
There is no central controller; Each layer processes sensor data andcontrols actuators unless...
...suppressed or inhibited by another layerThus, there is a dynamic hierarchy of layers
New behaviours implemented as new layers without modifyingexisting layers (evolutionary growth)
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 18 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnected
No representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some task
Robots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuators
Embodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its body
Simulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
“Intelligence without representation”: According to Brooks...
Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”
Methodology:
Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied
Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 19 / 25
Deficiencies
Scalability
BBR may be suitable for low-level tasks but may not scale to moresophisticated tasksSome form of representation may be required for tasks where themoment-to-moment sensory information is insufficient
Thus, hybrid behaviour-based / model-based approaches are popular[Arkin, 1998]
Yet, neither approach addresses the pervasive influence of uncertaintyin robotics
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 20 / 25
Deficiencies
Scalability
BBR may be suitable for low-level tasks but may not scale to moresophisticated tasks
Some form of representation may be required for tasks where themoment-to-moment sensory information is insufficient
Thus, hybrid behaviour-based / model-based approaches are popular[Arkin, 1998]
Yet, neither approach addresses the pervasive influence of uncertaintyin robotics
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 20 / 25
Deficiencies
Scalability
BBR may be suitable for low-level tasks but may not scale to moresophisticated tasksSome form of representation may be required for tasks where themoment-to-moment sensory information is insufficient
Thus, hybrid behaviour-based / model-based approaches are popular[Arkin, 1998]
Yet, neither approach addresses the pervasive influence of uncertaintyin robotics
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 20 / 25
Deficiencies
Scalability
BBR may be suitable for low-level tasks but may not scale to moresophisticated tasksSome form of representation may be required for tasks where themoment-to-moment sensory information is insufficient
Thus, hybrid behaviour-based / model-based approaches are popular[Arkin, 1998]
Yet, neither approach addresses the pervasive influence of uncertaintyin robotics
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 20 / 25
Deficiencies
Scalability
BBR may be suitable for low-level tasks but may not scale to moresophisticated tasksSome form of representation may be required for tasks where themoment-to-moment sensory information is insufficient
Thus, hybrid behaviour-based / model-based approaches are popular[Arkin, 1998]
Yet, neither approach addresses the pervasive influence of uncertaintyin robotics
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 20 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertainThe results of robot actions are uncertainThese uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertainThe results of robot actions are uncertainThese uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertain
The results of robot actions are uncertainThese uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertainThe results of robot actions are uncertain
These uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertainThe results of robot actions are uncertainThese uncertainties should be represented explicitly
We should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertainThe results of robot actions are uncertainThese uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
Probabilistic Robotics [Thrun et al., 2005]
Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)
Premise:
Perception is uncertainThe results of robot actions are uncertainThese uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds
“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 21 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robot
z — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observation
bel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movements
p(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
An Example: Localization
Localization is the problem of estimating position w.r.t. the globalreference frame
In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map
Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)
Notation:
x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x
Requires a map to know how likely an observation is at each location
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 22 / 25
Deficiencies?
Probabilistic robotics is the newest, most active paradigm in AR andis continuing to evolve at a fast pace; Thus, its success cannot yet befully characterized
Major challenge:
Navigation requires a mapThe representation of a probability distribution over all possible mapsrequires significant computational resources
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 24 / 25
Deficiencies?
Probabilistic robotics is the newest, most active paradigm in AR andis continuing to evolve at a fast pace; Thus, its success cannot yet befully characterized
Major challenge:
Navigation requires a mapThe representation of a probability distribution over all possible mapsrequires significant computational resources
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 24 / 25
Deficiencies?
Probabilistic robotics is the newest, most active paradigm in AR andis continuing to evolve at a fast pace; Thus, its success cannot yet befully characterized
Major challenge:
Navigation requires a map
The representation of a probability distribution over all possible mapsrequires significant computational resources
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 24 / 25
Deficiencies?
Probabilistic robotics is the newest, most active paradigm in AR andis continuing to evolve at a fast pace; Thus, its success cannot yet befully characterized
Major challenge:
Navigation requires a mapThe representation of a probability distribution over all possible mapsrequires significant computational resources
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 24 / 25
References
Arkin, R. (1998).
Behavior-Based Robotics.MIT Press.
Bekey, G. (2005).
Autonomous Robots: From Biological Inspiration to Implementation and Control.MIT Press.
Brooks, R. (1986).
A robust layered control system for a mobile robot.IEEE Journal of Robotics and Automation, 2(1):14–23.
Brooks, R. (1991).
Intelligence without representation.Artificial Intelligence, 47:139–159.
Cambridge Dictionary (2006).
Cambridge online dictionaries.
Dudek, G. and Jenkin, M. (2000).
Computational Principles of Mobile Robotics.Cambridge University Press.
Luger, G. and Stubblefield, W. (1998).
Artificial Intelligence: Structures and Strategies for Complex Problem Solving.Addison Wesley.
Thrun, S., Burgard, W., and Fox, D. (2005).
Probabilistic Robotics.MIT Press.
COMP 4766/6778 (MUN) Course Introduction January 12, 2009 25 / 25
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