Director: Prof. Maja J MatarićAssociate Director: Prof. Gaurav S. SukhatmeFounder: Prof. George A. Bekey

GOALS

Automate the process of robot controller design :Complexity of the robot’s tasks (sequencing)Robustness and real-time response propertiesModularity of the underlying architectureReusability of controller componentsSupport for complex task learning

[1] Monica N. Nicolescu, Maja J Matarić, "A hierarchical architecture for behavior-based robots", First International Joint Conference on Autonomous Agents and Multi-Agent Systems, July 15-19, 2002

[2] Monica N. Nicolescu, Maja J Matarić, "Learning and Interacting in Human-Robot Domains", Special Issue of IEEE Transactions on Systems, Man, and Cybernetics, Vol. 31, No.5, Pages 419-430, September, 2001.

[3] Monica N. Nicolescu, Maja J Matarić, "Experience-based representation construction: learning from human and robot teachers", IEEE/RSJ International Conference on Intelligent Robots and Systems, Pages 740-745, Oct. 29 – Nov 3, 2001

AN ACTION-BASED FRAMEWORK FOR LEARNING FROM DEMONSTRATION IN

HUMAN-ROBOT DOMAINS

APPROACH

Separate sensing (precondition checking) from actions into abstract/primitive behaviors.

allows for a more general set of activation conditionsEmbed abstract representation of the behavior’s goals

the task specific preconditions are tested via behavior links

Tasks are represented as (hierarchical) behavior networks.

Teaching by experienced demonstrationThe robot performs the task during demonstration and perceives the task through its own sensorsMapping observations to the robot’s own set of actions

A Hierarchical Abstract Behavior-Based ArchitectureRepresentation & execution of complex, sequential, hierarchically structured tasksSequential & opportunistic execution

EXPERIMENTAL VALIDATION

THE ARCHITECTURE

Effects Behi

{1/0}

Abstract/primitive behaviorstructure

Primitive behavior

Perform actions

Abstract behavior

Test world preconditions

Task specific preconditionsif met

Standard behaviorstructure

Test world preconditions

Test task specific preconditions

Perform actions

if met

if met

Effects Beh1…k {1/0}

Learned network:

Primitive Behavior

Abstract Behavior

Network Abstract Behavior

“Expanded” representation

of a NABNetwork link (ordering,

enabling, permanent)

Activation link

Execution: activation spreading + precondition checking

Behavior selection: a behavior is

active iff :

( It is not inhibited ) and

( Its controlled actuators are available) and

( Activation level 0 ) and

( All ordering constraints = TRUE ) and

( All permanent preconditions = TRUE ) and

(( All enabling preconditions = TRUE ) or

( the behavior was active in the previous step ))

Activation level (of a behavior):

The number of successor behaviors in the network that require the achievement of its postconditions

Generic network

THE LEARNING PROCESS

ALGORITHM:

Create network back-bone

The intervals Ik occurred during the demonstration

E

E

t1E t2E

C

C

t1C t1C

A

A

t1A t2A

A

A

t1D t2D

B

t1B t2B

B

A C A E

J Overlaps K

PermanentJ Includes K

J Ends K

No relationJ Starts K

J Equals K

EnablingJ Meets K

OrderingJ Before K

LinkRelationObservationJ K

J K

J K

J K

J K

J K

J K

Generate behavior links

Example network

Teacher-following strategy

The robot has a set of basic skills

Teacher signals moments in time relevant to the task

Mapping observations to the known effects of the robot’s own actions

An object transport task:

REFERENCES

Monica N. Nicolescu and Maja J. Matarić (monica|mataric@cs.usc.edu) http:||robotics.usc.edu|

~monica

Behavior A

Behavior B

Behavior A

Behavior B Behavior B

Behavior A

t1A t1A t1At2A t2A t2At1B t1B t1Bt2B t2B t2B

Permanent Enabling Ordering

Postconditions true

Behavior active

Network links types (sequential preconditions):

Behavior set: PickUp &

Drop colored objects, Track

colored targets

Learning in clean/cluttered

environments, from

human/robot teachers

A task with long sequences

A slalom task

An object transport task

A “gate-traversing” task

Teacher demonstration:

Teacher signals