混合刚柔机械臂实现复杂环境下的实时精准抓取
HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments
- 融合视觉与3D重建，基于形状感知规划安全路径
- 学习控制器使机械臂在未见过场景中误差低于2厘米
- 无需重训即可泛化到新环境，适合真实世界应用
随着机器人系统越来越多地在非结构化、杂乱且此前未见的环境中运行，对兼具柔性、适应性与精确控制的机械臂需求日益增长。本文提出一种实时混合刚-软连续体机械臂系统，用于在复杂环境中实现鲁棒的开放世界物体抓取。系统结合基于视觉的感知与3D场景重建，采用形状感知运动规划生成安全轨迹；基于学习的控制器驱动混合臂到达任意目标位姿，利用软段灵活性同时保持刚段精度。系统无需针对特定环境重训练，可直接推广至新场景。大量真实世界实验表明，在多种杂乱布局中均保持稳定抓取性能，平均误差低于2厘米，凸显了混合机械臂在非结构化环境中适应与可靠操作的潜力。
原文摘要 · Abstract (English)
As robotic systems increasingly operate in unstructured, cluttered, and previously unseen environments, there is a growing need for manipulators that combine compliance, adaptability, and precise control. This work presents a real-time hybrid rigid-soft continuum manipulator system designed for robust open-world object reaching in such challenging environments. The system integrates vision-based perception and 3D scene reconstruction with shape-aware motion planning to generate safe trajectories. A learning-based controller drives the hybrid arm to arbitrary target poses, leveraging the flexibility of the soft segment while maintaining the precision of the rigid segment. The system operates without environment-specific retraining, enabling direct generalization to new scenes. Extensive real-world experiments demonstrate consistent reaching performance with errors below 2 cm across diverse cluttered setups, highlighting the potential of hybrid manipulators for adaptive and reliable operation in unstructured environments.
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