We present Asymmetric Dexterity (AsymDex), a novel reinforcement learning (RL) framework that can efficiently learn asymmetric bimanual skills for multi-fingered hands without relying on demonstrations, which can be cumbersome to collect. Two crucial ingredients enable AsymDex to reduce the observation and action space dimensions and improve sample efficiency. First, AsymDex leverages the natural asymmetry found in human bimanual manipulation and assigns specific and interdependent roles to each hand: a facilitating hand that moves and reorients the object, and a dominant hand that performs complex manipulations on said object. Second, AsymDex defines and operates over relative observation and action spaces, facilitating responsive coordination between the two hands. Further, AsymDex can be easily integrated with recent advances in grasp learning to handle both the object acquisition phase and the interaction phase of bimanual dexterity. Unlike existing RL-based methods for bimanual dexterity, which are tailored to a specific task, AsymDex can be used to learn a wide variety of bimanual tasks that exhibit asymmetry. Detailed experiments on four simulated asymmetric bimanual dexterous manipulation tasks reveal that AsymDex consistently outperforms strong baselines that challenge its design choices, in terms of success rate and sample efficiency. The project website is at https://sites.google.com/view/asymdex-2024/.

翻译：我们提出非对称灵巧性（AsymDex），一种无需依赖繁琐采集的演示数据即可高效学习多指手部非对称双手技能的新型强化学习框架。两个关键设计使AsymDex能够降低观测与动作空间维度并提升采样效率。首先，AsymDex借鉴人类双手操控中天然存在的不对称性，为每只手分配特定且相互依赖的角色：辅助手负责移动和重定向物体，主导手则对该物体执行复杂操作。其次，AsymDex在相对观测与动作空间中进行定义和操作，促进双手间的响应式协调。此外，AsymDex可轻松整合抓握学习的最新进展，以处理双手灵巧操作中的物体获取阶段和交互阶段。与现有针对特定任务定制的双手灵巧强化学习方法不同，AsymDex可用于学习各类呈现不对称特征的双手任务。在四项模拟非对称双手灵巧操作任务上的详细实验表明，AsymDex在成功率和采样效率方面持续优于挑战其设计选择的强基线方法。项目网站位于 https://sites.google.com/view/asymdex-2024/。