Title :
Towards a Real-Time Bayesian Imitation System for a Humanoid Robot
Author :
Shon, Aaron P. ; Storz, Joshua J. ; Rao, Rajesh P N
Author_Institution :
Dept. of Comput. Sci. & Eng., Washington Univ., Seattle, WA
Abstract :
Imitation learning, or programming by demonstration (PbD), holds the promise of allowing robots to acquire skills from humans with domain-specific knowledge, who nonetheless are inexperienced at programming robots. We have prototyped a real-time, closed-loop system for teaching a humanoid robot to interact with objects in its environment. The system uses nonparametric Bayesian inference to determine an optimal action given a configuration of objects in the world and a desired future configuration. We describe our prototype implementation, show imitation of simple motor acts on a humanoid robot, and discuss extensions to the system
Keywords :
Bayes methods; closed loop systems; humanoid robots; knowledge acquisition; learning by example; nonparametric statistics; Bayesian imitation system; closed-loop system; domain-specific knowledge; humanoid robot; imitation learning; nonparametric Bayesian inference; programming by demonstration; skill acquisition; Automatic programming; Bayesian methods; Computer architecture; Encoding; Hidden Markov models; Humanoid robots; Humans; Prototypes; Real time systems; Robot programming;
Conference_Titel :
Robotics and Automation, 2007 IEEE International Conference on
Conference_Location :
Roma
Print_ISBN :
1-4244-0601-3
Electronic_ISBN :
1050-4729
DOI :
10.1109/ROBOT.2007.363903
