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

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

URL: http://arxiv.org/abs/2410.07787v2
Date: Fri, 11 Oct 2024 11:41:00 GMT
Title: Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning
Authors: Mariano Ramírez Montero, Ebrahim Shahabi, Giovanni Franzese, Jens Kober, Barbara Mazzolai, Cosimo Della Santina,
Abstract summary: Soft robots have the potential to revolutionize the use of robotic systems. Traditional rigid robots offer high accuracy and repeatability but lack the flexibility of soft robots. This work presents a novel hybrid robotic platform that integrates a rigid manipulator with a fully developed soft arm.
Score: 4.986982677009744
License: http://creativecommons.org/licenses/by/4.0/
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.

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