用视觉闭环控制软体藤蔓机器人，实现复杂环境自主导航与操作。
PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot
- 19个分布式摄像头提供全身视觉反馈，训练端到端视觉运动策略。
- 在分支结构、陡坡等挑战场景中成功导航，定位精度高且抗干扰。
- 适合对柔性机器人自主控制、多传感器融合感兴趣的开发者与研究者。
藤蔓机器人是一类具有柔顺身体和自支撑生长机制的软体机器人，适合在复杂受限环境中导航。然而，滞后效应、缆线相互作用和形变使其难以预测和建模，限制了传统规划与控制方法的有效性。本文提出首个自主藤蔓机器人系统，采用数据驱动、基于视觉的控制框架。系统在机器人本体上集成19个摄像头，获取机器人状态与周围环境的全面反馈。利用这一丰富的全身视觉信息，我们从示范数据中训练了一个端到端的视觉运动策略，实现复杂环境中的闭环自主控制。该策略能有效整合分布式感知信息，同时对不准确的机器人状态和执行误差保持鲁棒性。实验结果表明，所学策略在多种挑战性场景中表现稳健，包括穿越分支结构、攀爬斜坡、跨越无支撑地形、精确抓取目标物体，以及在狭小空间和障碍物间灵活穿行。项目主页 https://panovine-bot.github.io
原文摘要 · Abstract (English)
Vine robots, a class of soft, growing robots, are suitable for navigating complex and confined environments due to their compliant bodies and self-supporting growth mechanism. However, hysteresis, tether interactions, and deformations make them difficult to predict and model, which in turn limits the effectiveness of conventional planning and control approaches. In this work, we present a data-driven, vision-based control framework for the first autonomous vine robot system. Our system integrates 19 cameras distributed along the robot's body to provide comprehensive feedback of both the robot state and the surrounding environment. Using this rich whole-body vision feedback, we train an end-to-end visuomotor policy from demonstrations for closed-loop autonomous control in complex environments. The policy efficiently aggregates information from distributed sensing while maintaining robustness to inaccurate robot states and actuation. Experimental results demonstrate that the learned policy enables robust navigation and manipulation in challenging scenarios, including steering through branched structures, climbing up slopes, traversing unsupported terrain, reaching objects precisely, and maneuvering through confined spaces and obstacles. Project website https://panovine-bot.github.io
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