メタ学習と不確実性考慮型MPPIによる高速オンライン適応を伴う俊敏な移動制御
Agile Mobility with Rapid Online Adaptation via Meta-learning and Uncertainty-aware MPPI
メタ事前学習と不確実性を考慮したMPPIを組み合わせ、車輪型ロボットが少数の動力学データから迅速に適応し、未知のパラメータや路面変化に対応できる制御手法を提案した。
著者: Dvij Kalaria, Haoru Xue, Wenli Xiao, Tony Tao, Guanya Shi, John M. Dolan
分類: cs.RO
原文アブストラクト
Modern non-linear model-based controllers require an accurate physics model and model parameters to be able to control mobile robots at their limits. Also, due to surface slipping at high speeds, the friction parameters may continually change (like tire degradation in autonomous racing), and the controller may need to adapt rapidly. Many works derive a task-specific robot model with a parameter adaptation scheme that works well for the task but requires a lot of effort and tuning for each platform and task. In this work, we design a full model-learning-based controller based on meta pre-training that can very quickly adapt using few-shot dynamics data to any wheel-based robot with any model parameters, while also reasoning about model uncertainty. We demonstrate our results in small-scale numeric simulation, the large-scale Unity simulator, and on a medium-scale hardware platform with a wide range of settings. We show that our results are comparable to domain-specific well-engineered controllers, and have excellent generalization performance across all scenarios.
