通过识别物理交互实现接触丰富的机器人操作的演示一次性学习
One-Shot Learning from Demonstration of Contact-Rich Robotic Manipulation by Identifying Physical Interactions
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中文总结 AI 辅助
该研究提出一种明确建模物理交互的机器人操作演示学习方法，仅用单次演示即可复现复杂接触型任务，提升了对环境几何变化的鲁棒性、泛化性与在线自适应能力，填补了可解释少样本LfD的空白。
中文摘要 AI 辅助
演示学习（LfD）使机器人能够直接从人类处学习操作任务，从而支持机器人的多功能应用。大多数LfD方法未明确建模机器人与环境之间的物理交互，例如接触的建立与断开，而这些在操作任务中至关重要。由于相同的基本物理交互经常重复出现，它们可作为鲁棒、可泛化和自适应任务复现的基础。我们提出一种LfD方法，该方法明确利用发生在何处、何时的物理交互。利用该信息，混合位置-力控制器跟踪演示轨迹，直至满足来自演示的基于接触的转换条件。我们在包含开门和锁、螺栓拾取与拧紧、移位及表面轮廓加工的真实机器人实验中评估了该方法。我们表明，明确建模物理交互从四个方面有益于LfD：第一，仅使用单次演示且无任务先验知识即可复现复杂、顺序且接触丰富的操作任务；第二，增强对环境中未知几何变化的鲁棒性；第三，在几何变化已知时促进泛化；第四，利用任务复现过程中探索的几何信息促进在线自适应。我们讨论如何明确实现鲁棒性、泛化和适应性，而这在LfD文献中普遍缺失。因此，我们的工作旨在弥合机器人操作的可解释少样本LfD领域的空白。
英文摘要
Learning from Demonstration (LfD) allows robots to learn manipulation tasks directly from humans, thereby supporting the versatile application of robots. Most LfD methods do not explicitly model the physical interactions between a robot and its environment, such as the making and breaking of contact, while these are crucial during manipulation tasks. Because the same basic physical interactions recur often, they can be a basis for robust, generalizable, and adaptive task reproduction. We propose an LfD method that explicitly uses what physical interactions take place where and when. Using that information, a hybrid position-force controller tracks demonstrated trajectories until contact-based transition conditions from the demonstrations are met. We evaluate our method in real robot experiments consisting of opening doors and locks, bolt picking and screwing, dislodging, and surface contouring. We show that explicitly modeling physical interactions benefits LfD in four ways. First, by allowing reproduction of complex, sequential, and contact-rich manipulation tasks using only a single demonstration and no prior knowledge of the task. Second, by facilitating robustness to unknown geometric variations in the environment. Third, by facilitating generalization when geometric variations are known. Fourth, by facilitating online adaptation using geometric information explored during task reproduction. We discuss how robustness, generalization, and adaptivity can be explicitly implemented, which is generally lacking in the LfD literature. Thereby, our work aims to close a gap in interpretable few-shot LfD of robotic manipulation.
