微分可能シミュレータを用いた変形物体操作のための動的状態表現学習
DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator
微分可能シミュレータを訓練パイプラインに組み込み、変形物体の動的状態表現を学習する手法DiffSRLを提案し、軟体シミュレータ上のベンチマークで長期ダイナミクスと報酬予測の性能を実証した。
著者: Sirui Chen, Yunhao Liu, Jialong Li, Shang Wen Yao, Tingxiang Fan, Jia Pan
分類: cs.RO, cs.AI
原文アブストラクト
Dynamic state representation learning is an important task in robot learning. Latent space that can capture dynamics related information has wide application in areas such as accelerating model free reinforcement learning, closing the simulation to reality gap, as well as reducing the motion planning complexity. However, current dynamic state representation learning methods scale poorly on complex dynamic systems such as deformable objects, and cannot directly embed well defined simulation function into the training pipeline. We propose DiffSRL, a dynamic state representation learning pipeline utilizing differentiable simulation that can embed complex dynamics models as part of the end-to-end training. We also integrate differentiable dynamic constraints as part of the pipeline which provide incentives for the latent state to be aware of dynamical constraints. We further establish a state representation learning benchmark on a soft-body simulation system, PlasticineLab, and our model demonstrates superior performance in terms of capturing long-term dynamics as well as reward prediction.
