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.

研究表明，利用深度感知开发在各种操作任务中迅速适应的代理人可以在实现更好的通用性能和样本效率方面超越自动领域随机化和基于视觉策略的最新方法。

机器人机械臂的快速运动适应

Rapid Motor Adaptation for Robotic Manipulator Arms

Successful real-world deployment of legged robots would require them to adapt
in real-time to unseen scenarios like changing terrains, changing payloads,
wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to
solve this problem of real-time online adaptation in quadruped robots. RMA
consists of two components: a base policy and an adaptation module. The
combination of these components enables the robot to adapt to novel situations
in fractions of a second. RMA is trained completely in simulation without using
any domain knowledge like reference trajectories or predefined foot trajectory
generators and is deployed on the A1 robot without any fine-tuning. We train
RMA on a varied terrain generator using bioenergetics-inspired rewards and
deploy it on a variety of difficult terrains including rocky, slippery,
deformable surfaces in environments with grass, long vegetation, concrete,
pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across
diverse real-world as well as simulation experiments. Video results at
this https URL

该研究论文提出了一种名为 Rapid Motor Adaptation（RMA）的算法，用于解决四足机器人中的实时在线自适应问题。RMA 算法由基本策略和自适应模块两个组成部分组成，这两个部分的组合使机器人能够在几秒钟内适应新的情境，而无需使用任何领域知识。通过基于生物能源启发的奖励，我们在不同的地形上训练了 RMA 并在真实世界和模拟实验中展示了其卓越的性能。