There are many challenges in bimanual assembly, including high-level sequencing, multi-robot coordination, and low-level, contact-rich operations such as component mating. Task and motion planning (TAMP) methods, while effective in this domain, may be prohibitively slow to converge when adapting to disturbances that require new task sequencing and optimisation. These events are common during tight-tolerance assembly, where difficult-to-model dynamics such as friction or deformation require rapid replanning and reattempts. Moreover, defining explicit task sequences for assembly can be cumbersome, limiting flexibility when task replanning is required. To simplify this planning, we introduce a decentralised gradient-based framework that uses a piecewise continuous energy function through the automatic composition of adaptive potential functions. This approach generates sub-goals using only myopic optimisation, rather than long-horizon planning. It demonstrates effectiveness at solving long-horizon tasks due to the structure and adaptivity of the energy function. We show that our approach scales to physical bimanual assembly tasks for constructing tight-tolerance assemblies. In these experiments, we discover that our gradient-based rapid replanning framework generates automatic retries, coordinated motions and autonomous handovers in an emergent fashion.

翻译：双手机器人装配面临诸多挑战，包括高层级任务序列规划、多机器人协调以及低层级接触密集型操作（如部件配合）。任务与运动规划方法虽在该领域有效，但在适应需要新任务序列与优化的扰动时，其收敛速度可能过慢。这类事件在紧公差装配中十分常见，此时摩擦或变形等难以建模的动力学特性需要快速重规划与重新尝试。此外，为装配定义显式任务序列可能十分繁琐，限制了任务重规划所需的灵活性。为简化此类规划，我们提出一种去中心化梯度框架，该框架通过自适应势函数的自动组合构建分段连续能量函数。该方法仅通过短视优化而非长时域规划来生成子目标。得益于能量函数的结构与自适应性，该方法在解决长时域任务方面展现出有效性。我们证明该方法可扩展至物理双手机器人装配任务，用于构建紧公差装配体。在这些实验中，我们发现基于梯度的快速重规划框架能以涌现方式生成自动重试、协调运动与自主交接动作。