接触感知神经动力学
Contact-Aware Neural Dynamics
把现成物理仿真器当作基础先验、用真实触觉接触信号学习残差式神经动力学模型，这一思路可直接迁移到视觉驱动的机器人操作策略评估与精修，也可为视觉接触状态估计提供监督信号设计参考。
原文依据 we propose an implicit sim-to-real alignment framework that learns to directly align the simulator's dynamics with contact information
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论高保真物理仿真对于可扩展的机器人学习至关重要，但仿真到现实的差距依然存在，在涉及复杂、动态且不连续的交互（如物理接触）的任务中尤为突出。显式系统辨识通过调节显式仿真器参数来实现，但往往不足以对齐真实世界中错综复杂、高维且依赖状态的动力学。为克服这一难题，我们提出一种隐式仿真到现实对齐框架，学习将仿真器的动力学与接触信息直接对齐。该方法将现成仿真器视为基础先验，并学习一个接触感知神经动力学模型，利用真实世界观测对仿真状态进行修正。我们表明，使用机器人手部的触觉接触信息能够有效建模接触密集型任务中固有的非平滑不连续性，从而得到一个以真实世界数据为根基的神经动力学模型。我们进一步证明，该学习得到的前向动力学模型提升了状态预测精度，并可用于有效预测策略性能、精修仅在标准仿真器中训练的策略，为仿真到现实对齐提供了一种可扩展的数据驱动途径。
英文原文摘要 1217 字符
High-fidelity physics simulation is essential for scalable robotic learning, but the sim-to-real gap persists, especially for tasks involving complex, dynamic, and discontinuous interactions like physical contacts. Explicit system identification, which tunes explicit simulator parameters, is often insufficient to align the intricate, high-dimensional, and state-dependent dynamics of the real world. To overcome this, we propose an implicit sim-to-real alignment framework that learns to directly align the simulator's dynamics with contact information. Our method treats the off-the-shelf simulator as a base prior and learns a contact-aware neural dynamics model to refine simulated states using real-world observations. We show that using tactile contact information from robotic hands can effectively model the non-smooth discontinuities inherent in contact-rich tasks, resulting in a neural dynamics model grounded by real-world data. We demonstrate that this learned forward dynamics model improves state prediction accuracy and can be effectively used to predict policy performance and refine policies trained purely in standard simulators, offering a scalable, data-driven approach to sim-to-real alignment.