cognitive science 1 kartik talamadupula subbarao kambhampati j. benton dept. of computer science...
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CognitiveScience
1
Kartik TalamadupulaSubbarao
KambhampatiJ. Benton
Dept. of Computer ScienceArizona State University
Paul SchermerhornMatthias Scheutz
Cognitive Science Program
Indiana University
Planning for Human-Robot
Teaming
CognitiveScienceMotivationMotivation
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• Early motivation of AI– Autonomous control for
robotic agents
• Plenty of applications– Household Assistance– Search and Rescue– Military Drones and Mules
• All scenarios involve humans giving orders
• Planning must co-opt this area
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Human-Robot Teaming
• Teaming– Share the same
goal(s)– Autonomous behavior– Communication
• Role of Planning– Plan generation– Feedback
acceptance– Model resolution
HUMAN
ROBOT PLANNERPlanning and Execution
Monitoring
Human Robot Interaction (HRI)
Mixed Initiative Planning (MIP)
What are the factors that planners must take into account?
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Dimensions
Scenario / Environment
• Inspired by the real world• Large amounts of domain knowledge from
– Humans with experience– Technical documents and manuals
• New knowledge may arrive during execution– Planner must handle such contingencies
• Planner and Robot Features– Determined by the needs of the scenario– E.g.: NASA needs temporal planning
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Dimensions
Robotic Agent• Central Actor
– Execute actions– Gather sensory feedback
• Different types of robots– Various capabilities
Gripper
Humanoid Mobile Combined
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Dimensions
Human User
• Specifies and updates:– Scenario goals– Model (in some cases)
• Must be in communication with robot/system
NoviceUses the robot merely as an
assistant
Domain ExpertAuthority on the execution
environment
System ExpertAuthority on the
integrated AI system
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Planning
Goal Management
• Human-Robot Teaming– Utility stems from delegation of goals
• Support different types of goals– Temporal Goals: Deadlines– Priorities: Rewards and Penalties
• Bonus Goals: Partial Satisfaction
– Trajectory Goals– Conditional Goals
• Changes to goals on the fly– Open World Quantified Goals
[Talamadupula et al., AAAI 2010]
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• One true model of the world– Robot
• High + Low Level models
– Human User• Symbolic model + Add’l knowledge
– Planner must take this gap into account
• Model Maintenance v. Model Revision– Usability v. Consistency issues– Use the human user’s deep knowledge
• Distinct Models– Using two (or more) models
• Higher level: Task-oriented model• Lower level: Robot’s capabilities
Planning
Model Management
MODEL
Robot Human
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HRT Tasks: Examples
SEARCH AND REPORT
RECONNAISSANCE
KITCHEN ROBOT
ROBOT Mobile MobileMobile and Manipulator
HUMAN (USER) Domain ExpertSystem Expert
Novice
MODEL Less Dynamic DynamicHighly
Dynamic
GOALS Evolving Static Evolving
COMMUNICATIONNatural
LanguageAPIs
Natural Language
Feature
Task
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Case Study
Urban Search and Rescue
• Human-Robot Team in Urban Setting– Find and report location of critical assets– Human: Domain expert; removed from the
sceneSEARCH AND REPORT
• Deliver medical supplies
• Bonus Goal: Find and report injured humans
• Requirements– Updates to knowledge
base– Goal changes
[Talamadupula et. al., AAAI 2010]
RECONNAISSANCE
• Gather information• High risk to humans
– E.g. Bomb defusal
• Requirements– Support model changes– New capabilities
• E.g.: Zoom camera
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Goal Manager
Goal Manager MonitorMonitor PlannerPlanner
Plan
Plan
Problem Updates
Updated State
Information InitialModel
Information
Sensory Informati
on
Actions
System Integration
Additional Capabilities
Model Update
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Model Update: Demo Model Update: Demo RunRun
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• Initial GoalEnd of hallway
• During Execution Injured humans
(boxes) in rooms behind doors
• New action / effect during execution Push doors to get inside rooms
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Conclusions
• Human-Robot Teaming from a planning perspective
• Planning Challenges– Framework for Human-Robot Teaming Problems– Model and Goal Management
• Need to define the scope of planning for these tasks
– What are the main technical problems
• Huge potential for novel P&S applications• Companion Robots• Military and Service Drones• Household Assistants
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Future Work
• Multiple Models– Use two (or more) models to direct the planning
• Task v. Motion Level (BTAMP Workshops)• Classical v. More Expressive
• Robotic Proactiveness– “Ask” for help– Many sources of knowledge in the real world– Putting the “teaming” in HRT
• More Application Scenarios– Design planners sensitive to HRT issues
System DemoTuesday 5:30pm
Main Conference