四足机器人靠自身感知穿越微小障碍，无需外部传感器。
Robust Robot Walker: Learning Agile Locomotion over Tiny Traps
- 仅用身体感知信号，通过双阶段训练学习障碍隐式特征。
- 仿真与实测均成功穿越多种微小障碍，稳定性显著提升。
- 适合需高鲁棒性行走的野外或复杂环境机器人研究者。
四足机器人在实际应用中需具备稳健的行走能力。本文提出一种新方法，使四足机器人能够穿越各类微小障碍（即“微型陷阱”）。现有方法多依赖外感受传感器，对微小障碍检测不可靠。为此，本方法仅使用本体感受输入，引入包含接触编码器和分类头的两阶段训练框架，学习不同障碍的隐式表征。同时，设计专用奖励函数，提升训练稳定性并简化目标跟踪任务部署。为推动后续研究，我们构建了一个新的微型陷阱任务基准。大量仿真与真实场景实验验证了该方法的有效性与鲁棒性。
原文摘要 · Abstract (English)
Quadruped robots must exhibit robust walking capabilities in practical applications. In this work, we propose a novel approach that enables quadruped robots to pass various small obstacles, or "tiny traps". Existing methods often rely on exteroceptive sensors, which can be unreliable for detecting such tiny traps. To overcome this limitation, our approach focuses solely on proprioceptive inputs. We introduce a two-stage training framework incorporating a contact encoder and a classification head to learn implicit representations of different traps. Additionally, we design a set of tailored reward functions to improve both the stability of training and the ease of deployment for goal-tracking tasks. To benefit further research, we design a new benchmark for tiny trap task. Extensive experiments in both simulation and real-world settings demonstrate the effectiveness and robustness of our method. Project Page: https://robust-robot-walker.github.io/
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