Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Imitation in animals and artifacts
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Making reinforcement learning work on real robots
Making reinforcement learning work on real robots
Learning from observation using primitives
Learning from observation using primitives
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Teaching robots by moulding behavior and scaffolding the environment
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
The first segway soccer experience: towards peer-to-peer human-robot teams
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Multi-thresholded approach to demonstration selection for interactive robot learning
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Learning mobile robot motion control from demonstration and corrective feedback
Learning mobile robot motion control from demonstration and corrective feedback
Socially guided intrinsic motivation for robot learning of motor skills
Autonomous Robots
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Task demonstration is an effective technique for developing robot motion control policies. As tasks become more complex, however, demonstration can become more difficult. In this work, we introduce an algorithm that uses corrective human feedback to build a policy able to perform a novel task, by combining simpler policies learned from demonstration. While some demonstration-based learning approaches do adapt policies with execution experience, few provide corrections within low-level motion control domains or to enable the linking of multiple of demonstrated policies. Here we introduce Feedback for Policy Scaffolding (FPS) as an algorithm that first evaluates and corrects the execution of motion primitive policies learned from demonstration. The algorithm next corrects and enables the execution of a more complex task constructed from these primitives. Key advantages of building a policy from demonstrated primitives is the potential for primitive policy reuse within multiple complex policies and the faster development of these policies, in addition to the development of complex policies for which full demonstration is difficult. Policy reuse under our algorithm is assisted by human teacher feedback, which also contributes to the improvement of policy performance. Within a simulated robot motion control domain we validate that, using FPS, a policy for a novel task is successfully built from motion primitives learned from demonstration. We show feedback to both aid and enable policy development, improving policy performance in success, speed and efficiency.