Sparse incremental learning for interactive robot control policy estimation

— We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teloperation as our transfer scenario, we cast learning as statistical regression over sensor-actuator data pairs. Our desire for interactive learning necessitates algorithms that are incremental and realtime. We examine Locally Weighted Projection Regression, a popular robotic learning algorithm, and Sparse Online Gaussian Processes in this domain on one synthetic and several robot-generated data sets. We evaluate each algorithm in terms of function approximation, learned task performance, and scalability to large data sets.

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Daniel H. Grollman, Odest Chadwicke Jenkins
• Keywords: Data Sets, ICRA 2008, Interactive Learning Necessitates, Robotic Learning Algorithm, Robotics