REBot：用于瞬时动态避障的反射式规避机器人
REBot: Reflexive Evasion Robot for Instantaneous Dynamic Obstacle Avoidance
AI总结：
针对四足机器人在快速动态障碍下轨迹重规划失效的问题，提出基于有限状态机集成规避与恢复策略的REBot控制框架，实现瞬时低延迟避障，显著提升成功率与鲁棒性。
摘要：
动态避障（DOA）对于在存在移动障碍物或人类的环境中运行的四足机器人至关重要。现有方法通常依赖基于导航的轨迹重规划，这假设有充足的反应时间，当障碍物快速接近时会导致失败。在这些场景中，四足机器人需要反射式规避能力来执行瞬时、低延迟的机动。本文提出了反射式规避机器人（REBot），这是一种使四足机器人实现实时反射式避障的控制框架。REBot在有限状态机内集成了规避策略和恢复策略。通过精心设计的学习课程，并结合正则化和自适应奖励，REBot在瞬时DOA任务中实现了稳健的规避和快速稳定。我们通过大量仿真和真实世界实验验证了REBot，在避障成功率、能效以及对快速移动障碍物的鲁棒性方面展现出显著提升。视频和附录可在 https://rebot-2025.github.io/ 获取。
英文摘要：
Dynamic obstacle avoidance (DOA) is critical for quadrupedal robots operating in environments with moving obstacles or humans. Existing approaches typically rely on navigation-based trajectory replanning, which assumes sufficient reaction time and leading to fails when obstacles approach rapidly. In such scenarios, quadrupedal robots require reflexive evasion capabilities to perform instantaneous, low-latency maneuvers. This paper introduces Reflexive Evasion Robot (REBot), a control framework that enables quadrupedal robots to achieve real-time reflexive obstacle avoidance. REBot integrates an avoidance policy and a recovery policy within a finite-state machine. With carefully designed learning curricula and by incorporating regularization and adaptive rewards, REBot achieves robust evasion and rapid stabilization in instantaneous DOA tasks. We validate REBot through extensive simulations and real-world experiments, demonstrating notable improvements in avoidance success rates, energy efficiency, and robustness to fast-moving obstacles. Videos and appendix are available on https://rebot-2025.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
