対話型微分可能シミュレーション
Interactive Differentiable Simulation
剛体システムの物理特性を効率的かつ正確に推定できる微分可能物理エンジンIDSを提案し、視覚入力からのシステム同定やモデル予測制御への統合でサンプル効率を大幅に改善した。
著者: Eric Heiden, David Millard, Hejia Zhang, Gaurav S. Sukhatme
分類: cs.LG, cs.RO, cs.SY, eess.SY, stat.ML
原文アブストラクト
Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables. We introduce Interactive Differentiable Simulation (IDS), a differentiable physics engine, that allows for efficient, accurate inference of physical properties of rigid-body systems. Integrated into deep learning architectures, our model is able to accomplish system identification using visual input, leading to an interpretable model of the world whose parameters have physical meaning. We present experiments showing automatic task-based robot design and parameter estimation for nonlinear dynamical systems by automatically calculating gradients in IDS. When integrated into an adaptive model-predictive control algorithm, our approach exhibits orders of magnitude improvements in sample efficiency over model-free reinforcement learning algorithms on challenging nonlinear control domains.
