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.

翻译：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，尤其是在涉及复杂、动态且不连续交互（如物理接触）的任务中。显式系统辨识通过调整显式仿真器参数，通常不足以对齐现实世界中错综复杂、高维度且状态依赖的动力学特性。为克服此问题，我们提出了一种隐式的仿真到现实对齐框架，该框架学习直接利用接触信息将仿真器的动力学与真实世界对齐。我们的方法将现成的仿真器视为基础先验，并学习一个接触感知的神经动力学模型，以利用真实世界观测来修正仿真状态。我们证明，使用来自机器人手的触觉接触信息可以有效建模接触密集型任务中固有的非光滑不连续性，从而形成一个基于真实世界数据的神经动力学模型。我们进一步证明，这种学习得到的前向动力学模型提高了状态预测的准确性，并能有效用于预测策略性能以及优化仅在标准仿真器中训练的策略，从而为仿真到现实对齐提供了一种可扩展的、数据驱动的方法。