Agile Mobility with Rapid Online Adaptation via Meta-learning and Uncertainty-aware MPPI

2024-10-09

arXiv.org

AI Summary

High-speed极限 maneuvering of wheeled robots suffers from time-varying tire friction parameters (e.g., due to tire degradation), model mismatch, and slow online adaptation. Method: This paper proposes an adaptive control framework integrating meta-learning pretraining with uncertainty-aware Model Predictive Path Integral (MPPI) control. Leveraging Bayesian neural networks for dynamics modeling and probabilistic uncertainty quantification, the method achieves rapid cross-platform and cross-parameter adaptation using only minimal online data—without task- or robot-specific re-tuning. Results: Evaluated in numerical simulation, large-scale Unity simulation, and on a mid-sized physical robot platform, the approach matches the performance of hand-tuned controllers while significantly enhancing robustness and generalization against unknown dynamics and time-varying friction characteristics.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Rapid adaptation to changing friction parameters in mobile robots.
• Generalization of control across diverse wheel-based robot platforms.
• Efficient model learning with few-shot dynamics data and uncertainty reasoning.

Innovation

• Meta-learning for rapid robot adaptation
• Uncertainty-aware MPPI control strategy
• Few-shot dynamics data utilization