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.

翻译：发展通用化操作技能是具身智能领域的核心挑战，这包括在多样化任务配置中的泛化能力，涵盖物体形状、密度、摩擦系数等属性差异，以及施加于机器人的外力等外部干扰。快速运动适应（RMA）为此挑战提供了可行解决方案，其核心理念在于：影响智能体任务表现的关键隐变量（如物体质量与形状），可通过智能体的动作与本体感知历史记录有效推断。受RMA在运动控制与掌内旋转任务中的启发，我们利用深度感知技术开发面向多种操作任务的快速运动适应智能体。我们在Maniskill2基准测试中四项挑战性任务上评估了智能体性能，包括：基于YCB和EGAD数据集数百种物体的抓取放置操作、要求精确位姿控制的插销装配、以及多种水龙头与手柄的操作，并设置了定制化环境扰动。实验结果表明，我们的智能体在泛化性能与样本效率上均超越了自动域随机化、基于视觉策略等现有最优方法。