刚体模拟器会梦见软体机器人吗？学习肌腱驱动连续体机器人的接触丰富操作
Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots
AI总结：
针对肌腱驱动连续体机器人缺乏接触丰富操作模拟基础设施的问题，提出基于连续介质力学的离散化方法，将软体机器人原生集成到MuJoCo中，实现仿真到真实世界的零样本迁移。
摘要：
学习软体连续体机器人的接触丰富、全身操作受到缺乏加速刚体机器人操作的模拟基础设施的阻碍。现有的软体机器人模拟器具有物理基础，但缺乏接触丰富操作所需的接触处理、驱动支持或学习集成；刚体近似提供了这些能力，但牺牲了物理基础。我们通过推导一种基于连续介质力学的离散化方法，将软体机器人原生放置在MuJoCo内部，统一肌腱力、身体接触和动力学于单个物理管道中，从而弥合了肌腱驱动连续体机器人（TDCR）的这一差距。我们针对Cosserat杆参考（静态和动态）以及真实TDCR硬件验证了模拟器。然后，我们通过模拟中的遥操作训练基于状态的行为克隆策略，并在物理3段TDCR（安装在7自由度Franka臂上）上零样本部署到两个接触丰富操作任务中。据我们所知，这是连续体机器人接触丰富操作的仿真到现实迁移的首次演示。
英文摘要：
Learning contact-rich, whole-body manipulation for soft continuum robots is held back by the lack of simulation infrastructure that has accelerated rigid-robot manipulation. Existing soft robot simulators are physically grounded but lack the contact handling, actuation support, or learning integration needed for contact-rich manipulation; rigid-body approximations offer these capabilities but sacrifice physical grounding. We bridge this gap for tendon-driven continuum robots (TDCRs) by deriving a continuum-mechanics-informed discretization that places the soft robot natively inside MuJoCo, unifying tendon forces, body contact, and dynamics in a single physics pipeline. We validate the simulator against a Cosserat rod reference (static and dynamic) and real TDCR hardware. We then train state-based imitation learning policies via teleoperation in simulation and deploy them zero-shot to a physical 3-segment TDCR on a 7-DoF Franka arm across two contact-rich manipulation tasks. To our knowledge, this is the first demonstration of sim-to-real transfer for contact-rich manipulation with continuum robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
