RACER：轮式四足机器人竞速中基于采样的规划的残差自适应闭环估计

中文摘要 AI 辅助

我们提出了RACER，一个用于轮式四足机器人竞速的分层控制框架，它结合了MPPI规划器、学习得到的残差动力学模型以及低层RL速度跟踪器。规划器在标称的单轮运动学模型上增加了一个神经残差项，以捕捉RL策略的闭环跟踪行为。为了在有限的真实世界数据下训练该残差模型，我们提出了低秩残差自适应（LoRRA），一种两阶段方法，先在大型仿真数据上预训练以获得广泛覆盖，然后在小型真实世界数据集上使用低秩约束进行微调。在仿真中，我们通过以下结果实证验证了我们的工程选择：（A）残差动力学通过捕捉高速转弯时RL速度跟踪器的跟踪误差，提高了我们整个流程的整体性能。（B）使用源域和目标域数据训练的残差动力学，其竞速性能显著优于仅使用目标域数据训练的残差动力学。（C）在目标域自适应中，当地面系数或关节增益的域差距增大时，低秩约束比全量微调和从头训练能获得更高的成功率和更高的性能。

英文摘要

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.

发表机构

• University of California, Berkeley（加州大学伯克利分校）
• California Institute of Technology（加州理工学院）

机构由 AI 辅助整理，请以论文原文为准。