DexWrist：一种用于受限和动态操作的机械腕
DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation
AI总结：
DexWrist通过结合准直接驱动和解耦并行运动机制，在紧凑设计中实现高扭矩和动态接触任务，提升了受限环境中的操作性能。
摘要：
在开发灵活操作硬件时，主要关注手和夹具，但这些末端执行器通常与笨重且刚性大的腕部连接，限制了在人类环境中的性能。更多设计采用了反向驱动执行，但仍然由于耦合运动学或重链接的高机械惯性而难以建模和控制。我们提出了DexWrist，一种能够提高在高度受限环境中操作性能并实现动态、接触丰富的任务的机械腕。我们通过将准直接驱动执行与解耦并行运动机制结合，实现紧凑设计。它提供了3.75±0.05 Nm的额定扭矩，0.33±0.06 Nm的反向驱动扭矩，10.15±1.34 Hz的扭矩带宽，每个自由度±40度的活动范围，并在0.97 kg的包中实现电机到自由度的一一映射。在实践中，这些特性增加了在杂乱环境中的工作空间，并稳定了接触，而无需精细调优的阻抗控制。我们评估了DexWrist作为模拟和两个执行代表受限和接触丰富任务的机器人手臂的滴落式腕升级。在学习策略评估中，DexWrist在成功率上实现了50-76%的相对提升，并将自主任务完成时间减少了3-5倍。更多关于DexWrist的细节请访问https://dexwrist.csail.mit.edu。
英文摘要：
Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, these end-effectors are often paired with bulky and highly stiff wrists that limit performance in human environments. More recent designs have adopted backdrivable actuation, but are still difficult to model and control due to coupled kinematics or high mechanical inertia from heavy links. We present DexWrist, a compact robotic wrist combining quasi-direct-drive actuation with a decoupled parallel kinematic mechanism to advance manipulation in highly constrained environments and enable dynamic, contact-rich tasks. It delivers 3.75$\pm$0.05 Nm rated torque, 0.33$\pm$0.06 Nm backdrive torque, 10.15$\pm$1.34 Hz torque bandwidth, $\pm 40^\circ$ ROM in both DOFs, and a diagonal velocity-constraint Jacobian (one-to-one motor-to-DOF mapping) in a 0.97 kg package. In practice, these properties increase workspace in clutter and stabilize contact without finely tuned admittance control. We evaluate DexWrist as a drop-in upgrade in simulation and on three robot arms across constrained and contact-rich tasks. In learned policy evaluations on the AgileX PiPER and UR3e, DexWrist achieved 50-76% relative improvements in success rate and reduced autonomous task completion times by 3-5x; on a torque-controlled Franka FR3, where a strong joint-impedance baseline already succeeds, it still completed the task 1.4x faster. Project page and videos: https://martinpeticco.com/dexwrist
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。
