外观
论文arXiv 2610.0740923 天前
RACER：用于轮式四足竞速中基于采样规划的残差自适应闭环估计
原文链接：RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing（arXiv 2610.07409 · 发布日期：2026-10-05）
RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing
RACER面向轮式四足竞速，将MPPI、残差动力学与RL速度跟踪结合，并在仿真中验证性能收益。
核心要点
- 问题在于高速转弯时RL速度跟踪器存在跟踪误差，影响轮式四足竞速规划性能。
- 方法用神经残差项增强名义单轮车运动学模型，并提出LoRRA进行仿真预训练与少量真机数据低秩微调。
- 仿真结果显示，残差动力学能提升整体流程表现，捕捉高速转弯中的闭环跟踪行为。
- 同时使用源域与目标域数据训练优于仅用目标域数据；低秩适配在域差增大时优于全量微调和从头训练。
验证范围与局限：摘要中的实验验证范围为仿真；少量真机数据用于微调，未描述真机竞速验证结果。
英文摘要 (Abstract)
We present RACER, a hierarchical control framework for wheel-based quadruped racing that combines an MPPI planner with a learned residual dynamics model and a low-level RL velocity tracker. The planner augments a nominal unicycle kinematic model with a neural residual term to capture the closed-loop tracking behavior of the RL policy. To train this residual model under limited real-world data, we propose Low-Rank Residual Adaptation (LoRRA), a two-stage approach that pre-trains on large-scale simulation data for broad coverage and then fine-tunes on a small real-world dataset with a low-rank constraint. In simulation, we empirically validate our engineering choices by showing (A) Residual dynamics improve the overall performance of our pipeline by capturing the tracking error of RL velocity tracker at high-speed cornering. (B) Residual dynamics trained with both source-domain and target-domain data gives racing performance significantly better than the residual dynamics trained with only target-domain data. (C) Low-rank constraint at target-domain adaptation gives higher success rates and higher performance than full-tune and from-scratch when domain gap in ground coefficient or joint gain increases.
作者：Yuxiang Liu, Marla Eisman, Lizhi Yang, Aaron D. Ames, Francesco Borrelli
来源
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