Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from images. In this work, we demonstrate that locomotion policies trained with analytic gradients from a differentiable simulator can be successfully transferred to the real world. Typically, simulators that offer informative gradients lack the physical accuracy needed for sim-to-real transfer, and vice-versa. A key factor in our success is a smooth contact model that combines informative gradients with physical accuracy, ensuring effective transfer of learned behaviors. To the best of our knowledge, this is the first time a real quadrupedal robot is able to locomote after training exclusively in a differentiable simulation.

翻译：可微分仿真器提供解析梯度，使得样本效率更高的学习算法成为可能，并为从图像学习等数据密集型学习任务铺平道路。在本工作中，我们证明了利用可微分仿真器的解析梯度训练的运动策略能够成功迁移到现实世界。通常，提供信息梯度的仿真器缺乏实现仿真到现实迁移所需的物理精度，反之亦然。我们成功的一个关键因素在于一种平滑接触模型，它将信息梯度与物理精度相结合，确保了学习行为的有效迁移。据我们所知，这是首次有真实的四足机器人能够在完全于可微分仿真中训练后实现自主运动。