Learning robot motion control with demonstration and advice-operators

Abstract— As robots become more commonplace within society, the need for tools to enable non-robotics-experts to develop control algorithms, or policies, will increase. Learning from Demonstration (LfD) offers one promising approach, where the robot learns a policy from teacher task executions. Our interests lie with robot motion control policies which map world observations to continuous low-level actions. In this work, we introduce Advice-Operator Policy Improvement (AOPI) as a novel approach for improving policies within LfD. Two distinguishing characteristics of the A-OPI algorithm are data source and continuous state-action space. Within LfD, more example data can improve a policy. In A-OPI, new data is synthesized from a student execution and teacher advice. By contrast, typical demonstration approaches provide the learner with exclusively teacher executions. A-OPI is effective within continuous state-action spaces because high level human advice is translated into continuous-v...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Brenna Argall, Brett Browning, Manuela M. Veloso
• Keywords: Continuous State-action Spaces, IROS 2008, Robotics, Student Execution, Teacher Task Executions