An End-to-end Approach for Learning and Generating Complex Robot Motions from Demonstration

摘要

This paper proposes an end-to-end framework that can learn and decompose complex movements provided by a human demonstrator and generate new complex motions. Our approach analyzes the demonstration by the human expert and uses geometric criteria to decompose the observed movement into segments that are stored as dynamic movement primitives (DMPs) in a library. Then, given a new environment configuration, our system autonomously composes different motion primitives to construct an optimized trajectory that meets the constraints imposed by the new environment. Our system is therefore able to construct new DMPs to execute complex motions in environments that differ from the one where the original motions were taught. Our approach is also compatible with existing run-time obstacle avoidance approaches. We illustrate the application of our approach both in simulation and with a Baxter robot.