DiffSim2Real: 微分可能シミュレータのみで訓練した四足歩行ポリシーの実機展開
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
微分可能シミュレータで解析的勾配を用いて訓練した四足歩行ポリシーを、滑らかな接触モデルにより実世界に転移させることに成功した。
著者: Joshua Bagajo, Clemens Schwarke, Victor Klemm, Ignat Georgiev, Jean-Pierre Sleiman, Jesus Tordesillas, Animesh Garg, Marco Hutter
分類: cs.RO, cs.LG
原文アブストラクト
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.
