Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled by more than 40 muscles) of musculoskeletal sensory-motor circuits. In this work, we take inspiration from how human dexterity builds on a diversity of prior experiences, instead of being acquired through a single task. Motivated by this observation, we set out to develop agents that can build upon their previous experience to quickly acquire new (previously unattainable) behaviors. Specifically, our approach leverages multi-task learning to implicitly capture task-agnostic behavioral priors (MyoDex) for human-like dexterity, using a physiologically realistic human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately 3x more tasks, and 4x faster in comparison to a distillation baseline. While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors. We also demonstrate the effectiveness of our paradigms beyond musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit Hand. Website: https://sites.google.com/view/myodex

翻译：人类灵巧性是运动控制的标志。尽管肌肉骨骼感知运动回路复杂（多关节和多自由度，包含由40多块肌肉控制的23个关节），但人手仍能快速合成新行为。受人类灵巧性并非通过单一任务习得，而是建立在多样化先验经验基础上的观察启发，本工作旨在开发能依托过往经验快速获取新行为（先前无法实现）的智能体。具体而言，我们采用多任务学习范式，基于生理学真实的人手模型MyoHand，隐式捕获任务无关的行为先验（MyoDex），以模拟人类灵巧性。我们验证了MyoDex在小样本泛化中的有效性，以及其对大量未见灵巧操作任务的正向迁移能力。相较于蒸馏基线，采用MyoDex的智能体可解决约3倍数量的任务，且学习速度提升4倍。现有工作仅能合成单一千肌肉骨骼控制行为，而MyoDex是首个可泛化的操作先验，能催化多种接触密集行为的灵巧生理控制学习。此外，我们还证明了该范式在24自由度Adroit手灵巧性习得中的有效性，其应用范围超越肌肉骨骼控制。网址：https://sites.google.com/view/myodex