Towards a Real-Time Bayesian Imitation System for a Humanoid Robot

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.

• Type: Conference
• Year: 2007
• Where: ICRA
• Authors: Aaron P. Shon, Joshua J. Storz, Rajesh P. N. Rao
• Keywords: Humanoid Robot, ICRA 2007, Nonparametric Bayesian Inference, Robotics, Simple Motor Acts