提出高效可微机器人仿真算法，加速强化学习收敛。
End-to-End and Highly-Efficient Differentiable Simulation for Robotics
- 统一处理碰撞与摩擦的解析梯度计算
- 7自由度机械臂仅需5微秒，36自由度人形达95微秒
- 适合需要快速物理梯度的机器人优化任务
近年来，机器人仿真器在效率和可扩展性方面取得显著进步，可在数小时内生成数年模拟数据。然而，高效准确地计算仿真梯度仍是未解难题，尤其在涉及物理接触交互的问题中，对强化学习和轨迹优化算法的收敛速度具有潜在巨大提升。本文提出一种统一且高效的算法方案，用于计算机器人仿真器的解析梯度。该方法同时考虑碰撞与摩擦阶段，兼顾其固有的非光滑性，并利用多体系统带来的稀疏性。相关代码已用C++实现，并将在Simple仿真器中开源。实测性能达到：7自由度机械臂5微秒，36自由度人形机器人95微秒，较现有方案提速至少100倍。
原文摘要 · Abstract (English)
Over the past few years, robotics simulators have largely improved in efficiency and scalability, enabling them to generate years of simulated data in a few hours. Yet, efficiently and accurately computing the simulation derivatives remains an open challenge, with potentially high gains on the convergence speed of reinforcement learning and trajectory optimization algorithms, especially for problems involving physical contact interactions. This paper contributes to this objective by introducing a unified and efficient algorithmic solution for computing the analytical derivatives of robotic simulators. The approach considers both the collision and frictional stages, accounting for their intrinsic nonsmoothness and also exploiting the sparsity induced by the underlying multibody systems. These derivatives have been implemented in C++, and the code will be open-sourced in the Simple simulator. They depict state-of-the-art timings ranging from 5 microseconds for a 7-dof manipulator up to 95 microseconds for 36-dof humanoid, outperforming alternative solutions by a factor of at least 100.
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