introduction to robotics and ai - david · pdf filecrs' f3 robot testing a mobile phone,...
TRANSCRIPT
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Copyright © 2007 David Vernon (www.vernon.eu)
A Short Introduction to Robotics and AI
David Vernon
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Copyright © 2007 David Vernon (www.vernon.eu)
A Short Introduction to Robotics and AI
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Copyright © 2007 David Vernon (www.vernon.eu)
Learning Objectives• Nature of robotics• Robotic applications• Principal engineering issues• Principal AI issues• The future of robotics
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Copyright © 2007 David Vernon (www.vernon.eu)
The Word “Robot”
• “Robot” is derived from a Czech word meaning “forced labor”
• First appeared in a 1920 play R.U.R.(Rossum’s Universal Robots)by Czech playwright Karel Capek.
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Copyright © 2007 David Vernon (www.vernon.eu)
Definitions
• "A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks“
Robot Institute of America
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Copyright © 2007 David Vernon (www.vernon.eu)
Types of Robot• Manipulators
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Copyright © 2007 David Vernon (www.vernon.eu)
Unimate Puma Robot
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Copyright © 2007 David Vernon (www.vernon.eu)
Adept's SCARA robots, images courtesy of Adept Technology
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Copyright © 2007 David Vernon (www.vernon.eu)
Stäubli's RX130 manipulators, images
courtesy of Stäubli
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Copyright © 2007 David Vernon (www.vernon.eu)
CRS' F3 robot testing a mobile phone, image
courtesy of CRS Robotics
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Copyright © 2007 David Vernon (www.vernon.eu)
BH8-260 Hand from Barrett Technology, image courtesy of Barrett
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Copyright © 2007 David Vernon (www.vernon.eu)
The Space Station Remote Manipulator System (SSRMS)
Canadarm - Robot arm on every Space Shuttle, image courtesy of the Canadian Space Agency
•
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Copyright © 2007 David Vernon (www.vernon.eu)
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Copyright © 2007 David Vernon (www.vernon.eu)
Types of Robot• Manipulators• Mobile robots
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Copyright © 2007 David Vernon (www.vernon.eu)
Urban Robot Platform - image courtesy of NASA JPL
Urban Robot Platform - image courtesy of NASA JPL
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Copyright © 2007 David Vernon (www.vernon.eu)
iRobot-LE - image courtesy of iRobot
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Copyright © 2007 David Vernon (www.vernon.eu)
Oberon - underwater robot developed by the Australian Centre for Field Robotics
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Copyright © 2007 David Vernon (www.vernon.eu)
Dante II robot, image courtesy of NASA
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Copyright © 2007 David Vernon (www.vernon.eu)
Mars Sojourner, image courtesy of NASA
Urban Robot Platform - image courtesy of NASA JPL
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Copyright © 2007 David Vernon (www.vernon.eu)
Mars Sojourner, image courtesy of NASA
Urban Robot Platform - image courtesy of NASA JPL
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Copyright © 2007 David Vernon (www.vernon.eu)
Honda's P1 Humanoid Robot
(controlled via tether)
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Copyright © 2007 David Vernon (www.vernon.eu)
Honda's P3 Robot
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Copyright © 2007 David Vernon (www.vernon.eu)
Honda's ASIMO Robot
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Copyright © 2007 David Vernon (www.vernon.eu)
Honda's ASIMO Robot
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Copyright © 2007 David Vernon (www.vernon.eu)
Types of Robot• Manipulators• Mobile robots• Entertainment
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Copyright © 2007 David Vernon (www.vernon.eu)
Sony's 1st Generation Aibo Robot Dog
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Copyright © 2007 David Vernon (www.vernon.eu)
Sony's 2nd Generation Aibo Robot Dog
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Copyright © 2007 David Vernon (www.vernon.eu)
Types of Robot• Manipulators• Mobile robots• Entertainment• Education
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Copyright © 2007 David Vernon (www.vernon.eu)
PalmPilot Robot Kit, image courtesy of CMU's
Robotics Institute
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Copyright © 2007 David Vernon (www.vernon.eu)
Lego Mindstorms
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Copyright © 2007 David Vernon (www.vernon.eu)
Pioneer from activmedia.com
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Copyright © 2007 David Vernon (www.vernon.eu)
Types of Robot• Manipulators• Mobile robots• Entertainment• Education• AI robots
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Copyright © 2007 David Vernon (www.vernon.eu)
MIT AI Lab COG(with Rodney Brooks)
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Copyright © 2007 David Vernon (www.vernon.eu)
iCub(www.iCub.org)
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Copyright © 2007 David Vernon (www.vernon.eu)
iCub(www.iCub.org)
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Copyright © 2007 David Vernon (www.vernon.eu)
iCub(www.iCub.org)
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Copyright © 2007 David Vernon (www.vernon.eu)
Robotic Applications• Parts handling• Assembly• Painting• Surveillance• Security (bomb disposal … really telecherics
rather than robotics)• Home help (grass cutting, nursing)
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Copyright © 2007 David Vernon (www.vernon.eu)
Industrial Parts Delivery
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Copyright © 2007 David Vernon (www.vernon.eu)
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Copyright © 2007 David Vernon (www.vernon.eu)
CMU Nursebot project
FLO
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Copyright © 2007 David Vernon (www.vernon.eu)
CMU Nursebot project
PEARL
•
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Copyright © 2007 David Vernon (www.vernon.eu)
Pipe Inspection
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Copyright © 2007 David Vernon (www.vernon.eu)
Exploration
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Copyright © 2007 David Vernon (www.vernon.eu)
Engineering Issues
• Mechanical Construction• Control• Manipulation• Task Specification• Sensing• Path planning• Interaction• Reasoning• Autonomy and Adaptive Behaviour
Increasing AI
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Copyright © 2007 David Vernon (www.vernon.eu)
Mechanical Construction
•Controller
•Arm
•Drive
•End Effectors
•Sensor•Please click over the picture
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Copyright © 2007 David Vernon (www.vernon.eu)
Degrees of Freedom
ROTATE BASE OF ARMPIVOT BASE OF ARMBEND ELBOWWRIST UP AND DOWNWRIST LEFT AND RIGHTROTATE WRIST
•Please click over the picture
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Copyright © 2007 David Vernon (www.vernon.eu)
Task Specification & World Modelling •
Location of objects:-Links of manipulator, parts, tools
Specified by:-frame, coordinate systems
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Copyright © 2007 David Vernon (www.vernon.eu)
Trajectory Generation
How do I move from this location
To this location
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Copyright © 2007 David Vernon (www.vernon.eu)
Trajectory Generation
• Cartesian space vs. Joint Space• Manipulator kinematics (given joint angles, find position
and orientation of end effector)• Inverse kinematics (given position and orientation of
end effector, find joint angles)• Dynamics
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Copyright © 2007 David Vernon (www.vernon.eu)
Trajectory Generation• Location:
– Start location – End location– Intermediate location
• Interpolation– PTP-motion– Linear motion – Circular motion
• Dynamics– Velocity– Acceleration– Tool Functions and Settings
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Copyright © 2007 David Vernon (www.vernon.eu)
Trajectory Generation
• Problems with Cartesian space interpolation• Unreachable configurations• Multiple solutions
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Copyright © 2007 David Vernon (www.vernon.eu)
Task Specification & World Modelling •
Specification of orientation:
Use transformation matrices and Euler angles or Roll-Pitch-Yaw convention
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![Page 53: Introduction to Robotics and AI - David · PDF fileCRS' F3 robot testing a mobile phone, ... Robot (controlled via tether) ... Introduction to Robotics and AI.ppt](https://reader033.vdocument.in/reader033/viewer/2022051202/5a7923787f8b9a51548be99c/html5/thumbnails/53.jpg)
Copyright © 2007 David Vernon (www.vernon.eu)
Sensors
Pressure,temperature,humidity,touch
Tactual
(Under study)Taste
Gas sensorOlfactory
MicrowaveAuditory
Video camerasrange sensors
Visual
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Copyright © 2007 David Vernon (www.vernon.eu)
AI & Robotics
Interaction with the environment
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Copyright © 2007 David Vernon (www.vernon.eu)
The Real World
• Uncertain, Incomplete, Time-varying
From Introduction to AI Robotics, Robin Murphy
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Copyright © 2007 David Vernon (www.vernon.eu)
AI & Robotics
Agent based examples taken from:Q. Tipu, University of Southern California
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Copyright © 2007 David Vernon (www.vernon.eu)
AI & Robotics• How to represent knowledge about the world?
• How to react to new perceived events?• How to integrate new percepts to past experience?
• How to understand the user?• How to optimize balance between user goals & environment constraints?• How to use reasoning to decide on the best course of action?• How to communicate back with the user?
• How to plan ahead?• How to learn from experience?
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Copyright © 2007 David Vernon (www.vernon.eu)
General Architecture
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Copyright © 2007 David Vernon (www.vernon.eu)
AI & Robotics• Three ways to make controllers (Brain=computer=AI)
1. Most robots with rule-based controllers2. Neural networks3. Stimulus-Response Mechanism
- Subsumption Architecture (Brooks at MIT)- no memory and logical decision - hard-wired response to stimulation- COG
4. Intelligent Agents
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What is an (Intelligent) Agent?
• An over-used, over-loaded, and misused term.
• Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals.
• PAGE (Percepts, Actions, Goals, Environment)
• Task-specific & specialized: well-defined goals and environment
• The notion of an agent is meant to be a tool for analyzing systems, not an absolute characterization that divides the world into agents and non-agents. Much like, e.g., object-oriented vs. imperative program design approaches.
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Intelligent Agents and Artificial Intelligence
• Human mind as network of thousands or millions of agents all working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also contain many agents and systems for arbitrating among the agents' competing results.
• Distributed decision-making and control
• Challenges:– Action selection: What next action
to choose– Conflict resolution
sensors
effectors
Agency
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Agent Types
We can split agent research into two main strands:
• Distributed Artificial Intelligence (DAI) –Multi-Agent Systems (MAS) (1980 – 1990)
• Much broader notion of "agent" (1990’s – present)– interface, reactive, mobile, information
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Towards Autonomous Vehicles
http://iLab.usc.edu
http://beobots.org
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Interacting Agents
Lane Keeping Agent (LKA)• Goals: Stay in current lane• Percepts: Lane center, lane boundaries• Sensors: Vision• Effectors: Steering Wheel, Accelerator, Brakes• Actions: Steer, speed up, brake• Environment: Freeway
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Interacting Agents
Collision Avoidance Agent (CAA)• Goals: Avoid running into obstacles• Percepts: Obstacle distance, velocity, trajectory• Sensors: Vision, proximity sensing• Effectors: Steering Wheel, Accelerator, Brakes, Horn, Headlights• Actions: Steer, speed up, brake, blow horn, signal (headlights)• Environment: Freeway
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Conflict Resolution by Action Selection Agents
• Override: CAA overrides LKA
• Arbitrate: if Obstacle is Close then CAAelse LKA
• Compromise: Choose action that satisfies bothagents
• Any combination of the above
• Challenges:Doing the right thing
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The Right Thing = The Rational Action
• Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date
– Rational = Best ?– Rational = Optimal ?– Rational = Omniscience ? – Rational = Clairvoyant ?– Rational = Successful ?
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The Right Thing = The Rational Action
• Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date
– Rational = Best Yes, to the best of its knowledge– Rational = Optimal Yes, to the best of its abilities (incl. its
constraints)– Rational ≠ Omniscience – Rational ≠ Clairvoyant – Rational ≠ Successful
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Behavior and performance of IAs• Perception (sequence) to Action Mapping: f : P* → A
– Ideal mapping: specifies which actions an agent ought to take at any point in time
– Description: Look-Up-Table vs. Closed Form
• Performance measure: a subjective measure to characterize how successful an agent is (e.g., speed, power usage, accuracy, money, etc.)
• (degree of) Autonomy: to what extent is the agent able to make decisions and actions on its own?
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How is an Agent different from other software?
• Agents are autonomous, that is they act on behalf of the user • Agents contain some level of intelligence, from fixed rules to learning
engines that allow them to adapt to changes in the environment• Agents don't only act reactively, but sometimes also proactively• Agents have social ability, that is they communicate with the user, the
system, and other agents as required • Agents may also cooperate with other agents to carry out more complex
tasks than they themselves can handle • Agents may migrate from one system to another to access remote
resources or even to meet other agents
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Agent types
• Reflex agents• Reflex agents with internal states• Goal-based agents• Utility-based agents
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Reflex agents
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Reactive agents• Reactive agents do not have internal symbolic models. • Act by stimulus-response to the current state of the environment. • Each reactive agent is simple and interacts with others in a basic way. • Complex patterns of behavior emerge from their interaction.
• Benefits: robustness, fast response time • Challenges: scalability, how intelligent?
and how do you debug them?
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Reflex agents w/ state
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Goal-based agents
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Utility-based agents
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Anticipate
Assimilate Adapt
Action Perception
Cognition
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But there is a long way to go yet …
Perception ActionRepresentation
Cognition
‘Space of Interaction’
‘Space of Interaction’
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Phylogenetic self-organizing perceptuo-motor skills
Modulation circuit: homeostatic action selection by disinhibition of perceptuo-motor skills
Motivation(Amygdala)
Auto-associativeMemory
(Hippocampus)
ActionSelection
(Basal Ganglia)
Motor/SensoryAuto-associative
Memory
Sensory/MotorAuto-associative
Memory
simulated motor signals
simulated sensory signals
Prospection byaction simulation
Perturbation
Perturbation