arXiv 2509.236502026-06-23cs.RO

KiVi: 动觉-视觉空间整合用于动态且安全的自我中心腿部运动

KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric Legged Locomotion

MARMot Lab, National University of Singapore（新加坡国立大学MARMot实验室）；Center of X-Mechanics, Zhejiang University（浙江大学X力学中心）；Robot and Robot Intelligence Lab, Zhejiang University（浙江大学机器人与机器人智能实验室）

Peizhuo Li, Hongyi Li, Yuxuan Ma, Linnan Chang, Xinrong Yang, Ruiqi Yu, Shuhao Liao, Yifeng Zhang, Yuhong Cao, Qiuguo Zhu, Guillaume Sartoretti

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AI总结：

提出KiVi框架，通过分离动觉与视觉空间通路，以本体感觉为稳定主干并选择性整合视觉信息，实现四足机器人在复杂地形和户外环境中的鲁棒运动。

摘要：

基于视觉的运动在使腿式机器人感知和适应复杂环境方面显示出巨大潜力。然而，视觉信息本质上是脆弱的，容易受到遮挡、反射和光照变化的影响，这常常导致运动不稳定。受动物感觉运动整合的启发，我们提出了KiVi，一个动觉-视觉空间整合框架，其中动觉编码对身体运动的本体感觉，视觉空间推理捕获对周围地形的视觉感知。具体来说，KiVi分离这些通路，利用本体感觉作为稳定主干，同时选择性地整合视觉以进行地形感知和障碍物规避。这种模态平衡但整合的设计，结合记忆增强的注意力，使机器人能够鲁棒地解释视觉线索，同时通过本体感觉保持后备稳定性。大量实验表明，我们的方法使四足机器人能够稳定穿越各种地形，并在非结构化户外环境中可靠运行，对训练中未见过的分布外视觉噪声和遮挡保持鲁棒，从而突显了其在实际腿式运动中的有效性和适用性。项目页面：此 https URL

英文摘要：

Vision-based locomotion has shown great promise in enabling legged robots to perceive and adapt to complex environments. However, visual information is inherently fragile, being vulnerable to occlusions, reflections, and lighting changes, which often cause instability in locomotion. Inspired by animal sensorimotor integration, we propose KiVi, a Kinesthetic-Visuospatial integration framework, where kinesthetics encodes proprioceptive sensing of body motion and visuospatial reasoning captures visual perception of surrounding terrain. Specifically, KiVi separates these pathways, leveraging proprioception as a stable backbone while selectively incorporating vision for terrain awareness and obstacle avoidance. This modality-balanced, yet integrative design, combined with memory-enhanced attention, allows the robot to robustly interpret visual cues while maintaining fallback stability through proprioception. Extensive experiments show that our method enables quadruped robots to stably traverse diverse terrains and operate reliably in unstructured outdoor environments, remaining robust to out-of-distribution(OOD) visual noise and occlusion unseen during training, thereby highlighting its effectiveness and applicability to real-world legged locomotion. Project Page: https://marmotlab.github.io/kivi-quadruped/

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。