In this paper, we propose composable part-based manipulation (CPM), a novel
approach that leverages object-part decomposition and part-part correspondences
to improve learning and generalization of robotic manipulation skills. By
considering the functional correspondences between object parts, we
conceptualize functional actions, such as pouring and constrained placing, as
combinations of different correspondence constraints. CPM comprises a
collection of composable diffusion models, where each model captures a
different inter-object correspondence. These diffusion models can generate
parameters for manipulation skills based on the specific object parts.
Leveraging part-based correspondences coupled with the task decomposition into
distinct constraints enables strong generalization to novel objects and object
categories. We validate our approach in both simulated and real-world
scenarios, demonstrating its effectiveness in achieving robust and generalized
manipulation capabilities.

本论文提出了一种名为可组合的基于部件的操纵（CPM）的新方法，通过利用物体部件分解和部件间对应关系，以提高机器人操纵技能的学习和概括能力。通过考虑物体部件之间的功能对应关系，我们将功能性动作（如倾倒和约束放置）概念化为不同对应约束的组合。CPM 包括一系列可组合扩散模型，其中每个模型捕捉了不同物体间的对应关系。这些扩散模型可以根据特定的物体部件生成操作技能的参数。利用基于部件的对应关系以及将任务分解为不同约束能够实现对新对象和对象类别的强大概括。我们在模拟和真实环境下验证了我们的方法，并展示了它在实现稳健且广义操纵能力方面的有效性。