用元学习快速适配轮式机器人，实时处理摩擦变化。
Agile Mobility with Rapid Online Adaptation via Meta-learning and Uncertainty-aware MPPI
- 基于元学习预训练，仅需少量数据即可快速适应新机器人
- 在仿真和真实硬件上表现媲美专用控制器，泛化性强
- 考虑模型不确定性，适合高速动态场景的自适应控制
现代非线性模型预测控制依赖精确的物理模型与参数以实现移动机器人极限控制。然而，在高速行驶时，路面打滑会导致摩擦参数持续变化（如自动驾驶赛车中的轮胎磨损），控制器需快速适应。现有方法虽能为特定任务构建机器人模型并实现参数自适应，但需大量调参且难以迁移。本文提出一种基于元预训练的全模型学习控制器，仅需少量动态数据即可快速适配任意轮式机器人及参数，并具备模型不确定性推理能力。我们在小规模数值模拟、大规模Unity仿真以及中等规模硬件平台上验证了该方法，结果表明其性能可比肩领域专用优化控制器，且在各类场景下均展现出优异的泛化能力。
原文摘要 · Abstract (English)
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.
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