Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning

TL;DR

This paper introduces a hybrid robotic system combining soft and rigid components, leveraging imitation learning to perform flexible, precise, and generalizable contact-rich tasks effectively.

Contribution

The work presents a novel hybrid robot platform integrating soft and rigid elements with imitation learning for autonomous, adaptable task execution.

Findings

Achieved high generalizability in contact-rich tasks.

Combined softness and rigidity for improved flexibility and precision.

Demonstrated autonomous learning of complex manipulation skills.

Abstract

Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast, traditional rigid robots offer high accuracy and repeatability but lack the flexibility of soft robots. We argue that combining these characteristics in a hybrid robotic platform can significantly enhance overall capabilities. This work presents a novel hybrid robotic platform that integrates a rigid manipulator with a fully developed soft arm. This system is equipped with the intelligence necessary to perform flexible and generalizable tasks through imitation learning autonomously. The physical softness and machine learning enable our platform to achieve highly generalizable skills, while the rigid components ensure precision and repeatability.