软硬协同机器人通过模仿学习实现高泛化力操作
Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning
- 软硬机械臂融合，结合柔顺性与精度
- 基于模仿学习实现自主任务执行
- 适合复杂交互场景的智能机器人开发
软体机器人具备与环境安全、鲁棒且自适应交互的潜力，但其精确控制仍具挑战。传统刚性机器人虽精度高、可重复性强，却缺乏柔韧性。本文提出一种新型混合机器人平台，将刚性机械臂与完整开发的软臂集成，通过模仿学习赋予系统自主完成灵活通用任务的能力。物理上的柔软性与机器学习相结合，使平台具备高度泛化技能，而刚性部件则保障了操作精度与重复性。
原文摘要 · Abstract (English)
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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