论文/arXiv 2610.14753·18 天前

超越固定视觉历史：面向复杂地形四足运动的运动自适应时间感受野学习

TempoLoco针对固定深度历史难适配运动状态的问题，以本体感知动态聚合多尺度视觉记忆，并在仿真与Unitree B2真机复杂地形上验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 评测与安全

宇树 B2

原文链接：Beyond Fixed Visual History: Learning Motion-Adaptive Temporal Receptive Fields for Complex-Terrain Quadruped Locomotion（arXiv 2610.14753 · 发布日期：2026-10-10）

Beyond Fixed Visual History: Learning Motion-Adaptive Temporal Receptive Fields for Complex-Terrain Quadruped Locomotion

TempoLoco针对固定深度历史难适配运动状态的问题，以本体感知动态聚合多尺度视觉记忆，并在仿真与Unitree B2真机复杂地形上验证。

核心要点

• 现有视觉控制器用固定时间模式组织深度历史，忽略运动状态改变对应空间范围。
• TempoLoco从长深度缓冲学习多种互补时间视图，在共享特征空间编码。
• 框架利用本体感知历史和多尺度地形证据，动态聚合视觉记忆供单一策略使用。
• 实验成功率为78.4%，高于固定历史基线58.2%和均匀多尺度融合62.1%。

验证范围与局限：验证范围包括九类地形的仿真试验，以及带固定臂的Unitree B2真机试验。

英文摘要 (Abstract)

Versatile quadruped locomotion depends on a tight coupling between what the robot perceives and what its body can execute. Yet existing visual controllers typically organize depth history using fixed temporal patterns, overlooking that the same temporal span corresponds to different spatial ranges as the robot's speed, posture, and gait phase change. Consequently, the same visual history may be insufficient in one motion state but outdated or misaligned in another. To address this limitation, we propose TempoLoco, a body-aware dynamic visual perception framework that organizes visual memory according to the robot's current locomotion state. TempoLoco learns multiple complementary temporal views from a long depth buffer, encodes them within a shared feature space, and dynamically aggregates them using proprioceptive history and multi-scale terrain evidence. This enables a single policy to align visual anticipation with the robot's current motion capabilities across complex terrains, including stairs, platforms, gaps, discrete obstacles, rough ground, and slopes. From the experiment analysis, TempoLoco achieves a 78.4% success rate, compared with 58.2% for the strongest fixed-history baseline and 62.1% for uniform multi-scale fusion. In separate capability evaluations, it completes 41/45 simulated trials across nine terrain types and 23/25 real-world trials on a Unitree B2 with a fixed arm, demonstrating robust and deployable locomotion through body-aware dynamic visual perception.

作者：Hao Zhang, Yuping Yan, Yitian Hong, Luying Feng, Tianhong Wang, Fengqi Dai, Wei Chen, Yaochu Jin

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.14753：Beyond Fixed Visual History: Learning Motion-Adaptive Temporal Receptive Fields for Complex-Terrain Quadruped Locomotion访问 2026-10-21