想用一只机械手同时完成多个操作任务？DexCompose通过手指级分工和残差模块，在16项任务中达到77.4%成功率，解决了策略冲突问题。
DexCompose提出一种角色感知残差组合框架，通过显式手指级动作所有权解决多任务操作中的动作冲突。在16个复合任务（4项物体保持技能与4项下游交互）上测试，平均复合成功率达77.4%。该方法训练两个不对称残差模块：一个用于维持已有技能状态，另一个在新任务分配的子空间中调整下游策略。结果表明，结构化动作所有权与双重残差是超越传统策略链的灵巧技能组合方向。
DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand
Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging. Adding a new task on top of an existing manipulation skill often imposes conflicting demands on overlapping fingers and contact modes, causing destructive interference between preserving an existing manipulation outcome and executing a new one. We propose DexCompose, a role-aware residual composition framework that reuses pretrained dexterous policies for multi-task manipulation through explicit finger-level action ownership. Given two pretrained full-hand policies, DexCompose first collects successful post-task states from the first skill and performs release tests over candidate finger masks to identify which fingers are necessary for maintaining the established skill state. It then trains two asymmetric residual modules: a bounded residual stabilizer for task preservation, and a context-aware residual that adapts the frozen downstream policy only within the action subspace assigned to the new task. We evaluate the framework on 16 composite dexterous manipulation tasks spanning four object-retention skills and four downstream interactions. DexCompose achieves a 77.4% average composite success rate, demonstrating that structural action ownership with dual residuals offers a promising direction for composing dexterous skills beyond conventional policy chaining.