残差点接触学習による長期予測と不確実性伝播
Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners
剛体シミュレータの接触モデルを自己教師あり学習で補正する残差モデルを提案し、平面サイコロ転がしの長期予測で不確実性を伝播させて最先端手法と比較した論文。
著者: Nima Fazeli, Anurag Ajay, Alberto Rodriguez
分類: cs.RO
原文アブストラクト
The ability to simulate and predict the outcome of contacts is paramount to the successful execution of many robotic tasks. Simulators are powerful tools for the design of robots and their behaviors, yet the discrepancy between their predictions and observed data limit their usability. In this paper, we propose a self-supervised approach to learning residual models for rigid-body simulators that exploits corrections of contact models to refine predictive performance and propagate uncertainty. We empirically evaluate the framework by predicting the outcomes of planar dice rolls and compare it's performance to state-of-the-art techniques.
