1. Director: Prof. Maja J Matarić Associate Director: Prof. Gaurav S. Sukhatme Founder: Prof. George A. Bekey G OALS Automate the process of robot controller design :  Complexity of the robot’s tasks (sequencing)  Robustness and real-time response properties  Modularity of the underlying architecture  Reusability of controller components  Support 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 A N A CTION- B ASED F RAMEWORK FOR L EARNING FROM D EMONSTRATION IN H UMAN- R OBOT D OMAINS A PPROACH  Separate sensing (precondition checking) from actions into abstract/primitive behaviors.  allows for a more general set of activation conditions  Embed 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 demonstration  The robot performs the task during demonstration and perceives the task through its own sensors  Mapping observations to the robot’s own set of actions  A Hierarchical Abstract Behavior-Based Architecture  Representation & execution of complex, sequential, hierarchically structured tasks  Sequential & opportunistic execution E XPERIMENTAL V ALIDATION T HE A RCHITECTURE Effects Beh i {1/0} Abstract/primitive behavior structure Primitive behavior Perform actions Abstract behavior Test world preconditions Task specific preconditions if met Standard behavior structure Test world preconditions Test task specific preconditions Perform actions if met Effects Beh 1…k {1/0} Learned network: Primitive Behavior Abstract Behavior Network Abstract Behavior “Expanded” representation of a NAB Network 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 T HE L EARNING P ROCESS ALGORITHM:  Create network back-bone  The intervals I k occurred during the demonstration E E t1 E t2 E C C t1 C A A t1 A t2 A A A t1 D t2 D B t1 B t2 B B ACAE J Overlaps K Permanent J Includes K J Ends K No relation J Starts K J Equals K Enabling J Meets K OrderingJ Before K LinkRelationObservation JK JK 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: R EFERENCES 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 A t1 A t2 A t1 B t2 B PermanentEnablingOrdering 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