外观
论文arXiv 2608.069072 个月前
时空敏捷性：面向视觉引导动态四足拦截的时间约束强化学习
原文链接：Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception（arXiv 2608.06907 · 发布日期：2026-08-07）
Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception
针对四足机器人限时动态拦截，提出视觉预测结合位置与时间条件RL策略，并在接球实验中验证。
核心要点
- 现有速度跟踪式四足运动策略难以在严格时间约束下精准到达目标。
- 方法结合落点与时间预测视觉模块，以及直接以位置和时间为条件的RL运动策略。
- 系统集成多相机感知、在线轨迹预测、低延迟目标通信和Sim-to-Real运动控制。
- 相比速度跟踪基线，在测试设置下对指定落点和飞行时间的球取得更高接球成功率。
验证范围与局限：验证范围为四足机器人接球实验，球的预测落点在2米内、飞行时间为0.8至1.2秒。
英文摘要 (Abstract)
Legged robots require robust agility to perceive and interact with complex and dynamic environments within a constrained time. However, most existing quadruped locomotion works rely on velocity-tracking policy, which struggle to reach precise targets within strict temporal constraints. Moreover, integrating real-time perception with agile locomotion for highly dynamic targets remains challenging due to sensor latency and processing delays. To concretely study and benchmark such agility in dynamic settings, we introduce a challenging ball-catching task for legged robots. This paper proposes an integrated framework that combines a vision module for landing point and time prediction with a direct position and time conditioned RL locomotion policy, instead of intermediate velocity commands. Beyond the method design, this work presents a system-level contribution that completes real-time robotic interception system that integrates multi-camera perception, online trajectory prediction, low-latency target communication, and sim-to-real locomotion control into a closed-loop deployment pipeline. By explicitly predicting the future spatial-temporal target, our approach mitigates perception latency during dynamic interception. We conducted extensive ball-catching experiments for the legged robot. Through comparative experiments against a velocity-tracking baseline, our direct target-conditioned approach achieves a higher success rate in catching balls with predicted landing spots within 2 meters and flight times between 0.8 and 1.2 seconds. This shows that the robot has successfully completed the dynamic ball-catching task under our tested setup. Furthermore, our policy exhibits a smaller performance gap after deployment, suggesting improved sim-to-real behavior in these trials.
作者：Yidong Zhu, Zibo Dai, T Zhang, Leixin Chang, Hua Chen
来源
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