用于机器人操作臂的快速运动自适应
Rapid Motor Adaptation for Robotic Manipulator Arms
AI总结：
本文提出利用深度感知和Rapid Motor Adaptation方法，在Maniskill2基准的四类操作任务上训练机器人操作臂智能体，实验证明其在泛化性能和样本效率上优于自动域随机化和视觉策略方法。
摘要：
在具身人工智能中，开发可泛化的操作技能是一项核心挑战。这包括跨多样化任务配置的泛化，涵盖物体形状、密度、摩擦系数的变化，以及施加于机器人的外力等外部扰动。Rapid Motor Adaptation (RMA) 为这一挑战提供了一种有前景的解决方案。它假设影响智能体任务表现的关键隐藏变量（如物体质量和形状）可以从智能体的动作和本体感觉历史中有效推断出来。受运动控制和手中旋转中 RMA 的启发，我们利用深度感知来开发面向多种操作任务中快速运动自适应的智能体。我们在 Maniskill2 基准中的四个具有挑战性的任务上评估了我们的智能体，即使用来自 YCB 和 EGAD 数据集的数百个物体进行抓取-放置操作、具有精确位置和姿态要求的销钉插入，以及操作各种水龙头和把手，并设置了定制化的环境变化。实验结果表明，我们的智能体超越了自动域随机化和基于视觉的策略等最先进方法，获得了更好的泛化性能和样本效率。
英文摘要：
Developing generalizable manipulation skills is a core challenge in embodied AI. This includes generalization across diverse task configurations, encompassing variations in object shape, density, friction coefficient, and external disturbances such as forces applied to the robot. Rapid Motor Adaptation (RMA) offers a promising solution to this challenge. It posits that essential hidden variables influencing an agent's task performance, such as object mass and shape, can be effectively inferred from the agent's action and proprioceptive history. Drawing inspiration from RMA in locomotion and in-hand rotation, we use depth perception to develop agents tailored for rapid motor adaptation in a variety of manipulation tasks. We evaluated our agents on four challenging tasks from the Maniskill2 benchmark, namely pick-and-place operations with hundreds of objects from the YCB and EGAD datasets, peg insertion with precise position and orientation, and operating a variety of faucets and handles, with customized environment variations. Empirical results demonstrate that our agents surpass state-of-the-art methods like automatic domain randomization and vision-based policies, obtaining better generalization performance and sample efficiency.
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。
