Cognitive
Science
1
Kartik
Talamadupula
Subbarao
Kambhampati
J. Benton
Dept. of Computer Science
Arizona State University
Paul
Schermerhorn
Matthias
Scheutz
Cognitive Science Program
Indiana University
Planning for
Human
-
Robot Teaming
Cognitive
Science
Motivation
2
•
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
Cognitive
Science
Human
-
Robot Teaming
•
Teaming
–
Share the same goal(s)
–
Autonomous behavior
–
Communication
•
Role of Planning
–
Plan
generation
–
Feedback
acceptance
–
Model
resolution
HUMAN
ROBOT
PLANNER
Planning and Execution
Monitoring
Human Robot Interaction
(HRI)
Mixed Initiative Planning
(MIP)
What are the
factors
that planners must take into account?
Cognitive
Science
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
Cognitive
Science
Dimensions
Robotic Agent
•
Central Actor
–
Execute actions
–
Gather sensory feedback
•
Different types of robots
–
Various capabilities
Gripper
Humanoid
Mobile
Combined
Cognitive
Science
Dimensions
Human User
•
Specifies and updates:
–
Scenario goals
–
Model (in some cases)
•
Must be in communication with robot/system
Novice
Uses the robot merely as
an assistant
Domain Expert
Authority on the
execution environment
System Expert
Authority on the
integrated AI system
Cognitive
Science
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]
Cognitive
Science
•
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
Cognitive
Science
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
Feature
Task
Cognitive
Science
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 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
Cognitive
Science
Goal
Manager
Monitor
Planner
Plan
Plan
Problem
Updates
Updated State
Information
Initial
Model
Information
Sensory
Information
Actions
System Integration
Additional
Capabilities
Model
Update
Cognitive
Science
Model Update: Demo Run
12
•
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
Cognitive
Science
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
Cognitive
Science
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
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