視覚デモンストレーションからのモデルベース逆強化学習
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
事前学習した視覚ダイナミクスモデルを用い、人間の視覚デモのみからコスト関数を学習し、視覚モデル予測制御で物体操作を再現するフレームワークを実機で検証した。
著者: Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier
分類: cs.RO, cs.LG
原文アブストラクト
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good dynamics models, developing algorithms that scale to high-dimensional state-spaces and being able to learn from both visual and proprioceptive demonstrations. In this work, we present a gradient-based inverse reinforcement learning framework that utilizes a pre-trained visual dynamics model to learn cost functions when given only visual human demonstrations. The learned cost functions are then used to reproduce the demonstrated behavior via visual model predictive control. We evaluate our framework on hardware on two basic object manipulation tasks.
