Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior. In this work, we resolve this conflict with a smooth, implicit encoding of the structure inherent to contact-induced discontinuities. Our method, ContactNets, learns parameterizations of inter-body signed distance and contact-frame Jacobians, a representation that is compatible with many simulation, control, and planning environments for robotics. We furthermore circumvent the need to differentiate through stiff or non-smooth dynamics with a novel loss function inspired by the principles of complementarity and maximum dissipation. Our method can predict realistic impact, non-penetration, and stiction when trained on 60 seconds of real-world data.

中文翻译：

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ContactNets：使用平滑、隐式表示学习不连续的接触动力学

学习机器人动力学的常用方法假设运动是连续的，这会导致对经历不连续冲击和静摩擦行为的系统进行不切实际的模型预测。在这项工作中，我们通过对接触引起的不连续性固有的结构进行平滑、隐式编码来解决这一冲突。我们的方法 ContactNets 学习身体间符号距离和接触框架雅可比矩阵的参数化，这种表示与机器人的许多模拟、控制和规划环境兼容。我们进一步规避了通过互补和最大耗散原则启发的新颖损失函数区分僵硬或非平滑动力学的需要。当在 60 秒的真实世界数据上训练时，我们的方法可以预测真实的影响、非渗透和静摩擦。