Planning for Human-Robot Teaming - Subbarao Kambhampati

bouncerarcheryΤεχνίτη Νοημοσύνη και Ρομποτική

14 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

48 εμφανίσεις

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