対照学習による変形可能物体の予測表現学習
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
対照推定を用いて視覚表現とダイナミクスモデルを同時最適化し、ロープや布の操作をモデルベース計画で実現、シミュレーションから実機PR2へ転移した研究。
著者: Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, Lerrel Pinto
分類: cs.LG, cs.CV, cs.RO, stat.ML
原文アブストラクト
Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models. In this work, we propose a new learning framework that jointly optimizes both the visual representation model and the dynamics model using contrastive estimation. Using simulation data collected by randomly perturbing deformable objects on a table, we learn latent dynamics models for these objects in an offline fashion. Then, using the learned models, we use simple model-based planning to solve challenging deformable object manipulation tasks such as spreading ropes and cloths. Experimentally, we show substantial improvements in performance over standard model-based learning techniques across our rope and cloth manipulation suite. Finally, we transfer our visual manipulation policies trained on data purely collected in simulation to a real PR2 robot through domain randomization.
