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Kartik Talamadupula Subbarao Kambhampati J. Benton

Planning for Human-Robot Teaming. Kartik Talamadupula Subbarao Kambhampati J. Benton Dept. of Computer Science Arizona State University. Paul Schermerhorn Matthias Scheutz Cognitive Science Program Indiana University. Motivation. Early motivation of AI

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Kartik Talamadupula Subbarao Kambhampati J. Benton

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  1. Planning for Human-Robot Teaming KartikTalamadupula SubbaraoKambhampati J. Benton Dept. of Computer ScienceArizona State University Paul Schermerhorn Matthias Scheutz Cognitive Science ProgramIndiana University

  2. 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

  3. Human-Robot Teaming • Teaming • Share the same goal(s) • Autonomous behavior • Communication • Role of Planning • Plan generation • Feedback acceptance • Model resolution HUMAN Human Robot Interaction (HRI) Mixed Initiative Planning (MIP) ROBOT PLANNER Planning and Execution Monitoring What are the factors that planners must take into account?

  4. 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

  5. Gripper Humanoid Mobile Combined DimensionsRobotic Agent • Central Actor • Execute actions • Gather sensory feedback • Different types of robots • Various capabilities

  6. DimensionsHuman User • Specifies and updates: • Scenario goals • Model (in some cases) • Must be in communication with robot/system Novice Domain Expert System Expert Uses the robot merely as an assistant Authority on the execution environment Authority on the integrated AI system

  7. 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 Goals[Talamadupula et al., AAAI 2010]

  8. MODEL Robot Human PlanningModel Management • 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

  9. HRT Tasks: Examples Task Feature

  10. 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

  11. Updated State Information Plan Goal Manager Monitor Planner Problem Updates Plan System Integration Model Update Additional Capabilities Actions Sensory Information InitialModel Information

  12. 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

  13. 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

  14. 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 5:30pmMain Conference

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