Learning Beyond Rigid Manipulation with Hybrid Soft-Rigid Robots
Learning Beyond Rigid Manipulation with Hybrid Soft-Rigid Robots
This paper introduces a hybrid robotic system combining a rigid arm and a soft manipulator. The authors propose a tailored imitation learning algorithm that leverages the complementary strengths of rigid precision and soft compliance to acquire manipulation skills and generalize them across task variations. Results show the system matches or exceeds rigid robot performance in classical tasks like stacking while enabling new behaviors such as grasping objects through narrow openings.
The authors present a hybrid rigid-soft robotic system and demonstrate via imitation learning that it can perform classical manipulation tasks with performance matching or exceeding rigid robots while enabling new behaviors like grasping through narrow openings.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org