不整地環境における計画のための地形認識ダイナミクスのオンライン適応
Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments
関数エンコーダを用いて、多様な地形上のロボットダイナミクスを表現するニューラルネットワーク基底関数を学習し、新しい地形にオンラインで迅速に適応する手法を提案。シミュレータ実験で、ベースラインより衝突が少なくナビゲーション性能が向上することを示した。
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著者: William Ward, Sarah Etter, Tyler Ingebrand, Christian Ellis, Adam J. Thorpe, Ufuk Topcu
分類: cs.RO
原文アブストラクト
Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a critical challenge becomes estimating the robot's dynamics on changing terrain in order to enable reliable, accurate navigation and planning. We present a novel online adaptation approach for terrain-aware dynamics modeling and planning using function encoders. Our approach efficiently adapts to new terrains at runtime using limited online data without retraining or fine-tuning. By learning a set of neural network basis functions that span the robot dynamics on diverse terrains, we enable rapid online adaptation to new, unseen terrains and environments as a simple least-squares calculation. We demonstrate our approach for terrain adaptation in a Unity-based robotics simulator and show that the downstream controller has better empirical performance due to higher accuracy of the learned model. This leads to fewer collisions with obstacles while navigating in cluttered environments as compared to a neural ODE baseline.
