DiffSkill: 微分可能物理に基づくスキル抽象化による道具を用いた変形物体操作
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
微分可能物理シミュレータで短期的な道具操作スキルを獲得し、RGBD画像からスキルを抽象化するニューラルネットを学習して、長期的な変形物体操作タスクを計画・実行するフレームワークを提案した。
著者: Xingyu Lin, Zhiao Huang, Yunzhu Li, Joshua B. Tenenbaum, David Held, Chuang Gan
分類: cs.LG, cs.CV, cs.GR, cs.RO
原文アブストラクト
We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorithms for deformable object manipulation. However, such gradient-based trajectory optimization typically requires access to the full simulator states and can only solve short-horizon, single-skill tasks due to local optima. In this work, we propose a novel framework, named DiffSkill, that uses a differentiable physics simulator for skill abstraction to solve long-horizon deformable object manipulation tasks from sensory observations. In particular, we first obtain short-horizon skills using individual tools from a gradient-based optimizer, using the full state information in a differentiable simulator; we then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input. Finally, we plan over the skills by finding the intermediate goals and then solve long-horizon tasks. We show the advantages of our method in a new set of sequential deformable object manipulation tasks compared to previous reinforcement learning algorithms and compared to the trajectory optimizer.
