Kartik Talamadupula

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Planning for Human-Robot Teaming
Kartik Talamadupula Subbarao Kambhampati J.
Benton Dept. of Computer ScienceArizona State
University
Paul Schermerhorn Matthias Scheutz Cognitive
Science ProgramIndiana University
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Motivation

• 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

What are the factors that planners must take into
account?
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DimensionsScenario / 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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DimensionsRobotic Agent

• Central Actor
• Execute actions
• Gather sensory feedback
• Different types of robots
• Various capabilities

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DimensionsHuman User

• Specifies and updates
• Scenario goals
• Model (in some cases)
• Must be in communication with robot/system

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PlanningGoal 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 GoalsTalamadupula et al.,
  AAAI 2010

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PlanningModel Management

• One true model of the world
• Robot
• High Low Level models
• Human User
• Symbolic model Addl knowledge
• Planner must take this gap into account
• Model Maintenance v. Model Revision
• Usability v. Consistency issues
• Use the human users deep knowledge
• Distinct Models
• Using two (or more) models
• Higher level Task-oriented model
• Lower level Robots capabilities

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HRT Tasks Examples
Search and Report Reconnaissance Kitchen Robot
Robot Mobile Mobile Mobile and Manipulator
Human (User) Domain Expert System Expert Novice
Model Less Dynamic Dynamic Highly Dynamic
Goals Evolving Static Evolving
Communication Natural Language APIs Natural Language
Task
Feature
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Case StudyUrban Search and Rescue

• Human-Robot Team in Urban Setting
• Find and report location of critical assets
• Human Domain expert removed from the scene

• SEARCH 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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System Integration
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Model Update Demo Run

• Initial Goal
• End 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 PS 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 Demo Tuesday 530pmMain Conference