Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation

TL;DR

This paper introduces residual Learning from Demonstration (rLfD), a framework combining Dynamic Movement Primitives with Reinforcement Learning to improve contact-rich manipulation tasks like peg-in-hole insertions, enhancing success and generalization.

Contribution

The paper proposes a novel residual learning framework that adapts DMPs with RL for contact-rich tasks, enabling better performance and transferability in manipulation.

Findings

Residual learning in task space improves DMP performance.

rLfD enhances task success and generalization.

Framework enables few-shot transfer to different geometries and frictions.

Abstract

Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can learn such skills. Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC) but can struggle in the context of insertion. Policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. However, it is not clear how to best do this with DMPs. As a result, we consider several possible ways for adapting a DMP formulation and propose ``residual Learning from Demonstration`` (rLfD), a framework that combines DMPs with Reinforcement Learning (RL) to learn a residual correction policy. Our evaluations suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs \rb{and enables transfer to different geometries and frictions through few-shot task adaptation}. The proposed framework is evaluated on a set of tasks. A simulated robot and a physical robot have to successfully insert pegs, gears and plugs into their respective sockets. Other material and videos accompanying this paper are provided at https://sites.google.com/view/rlfd/.