软腕通过柔性接触实现稳定插入，自动恢复失败。
Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery
- 用被动柔顺腕部吸收冲击，无需高频控制或力传感器。
- 模拟中成功率83%，可应对5°错位、20mm孔位偏差等复杂扰动。
- 结合视觉语言模型自动识别错误并调用恢复策略，适合真实机器人部署。
物体插入任务在姿态不确定和环境变化下易失败，通常需手动微调或重新训练控制器。本文提出一种基于被动柔顺软腕的鲁棒且抗扰插入方法，通过大变形吸收安全接触，无需高频控制或力感知。将插入过程建模为柔顺驱动的接触形成序列，逐步约束自由度，并集成自动化失败恢复机制。核心洞察是腕部柔顺支持安全多次尝试恢复，故称‘柔顺驱动的失败恢复’。采用预训练视觉语言模型（VLM），从末端姿态与图像评估每项技能执行，识别失败模式，并通过选择新技能与更新目标提出恢复动作。模拟实验中，方法在随机扰动下取得83%成功率，包括最大5°抓取偏移、20 mm孔位误差、摩擦系数增加五倍及未见过的方形/矩形插头；进一步在真实机器人上验证了有效性。
原文摘要 · Abstract (English)
Object insertion tasks are prone to failure under pose uncertainty and environmental variation, often requiring manual fine-tuning or controller retraining. We present a novel approach for robust and resilient object insertion using a passively compliant soft wrist that enables safe contact absorption through large deformations, without high-frequency control or force sensing. Our method structures insertion as compliance-enabled contact formations, sequential contact states that progressively constrain degrees of freedom, and integrates automated failure recovery strategies. Our key insight is that wrist compliance permits safe, repeated recovery attempts; hence, we refer to it as compliance-enabled failure recovery. We employ a pre-trained vision-language model (VLM) that assesses each skill execution from terminal poses and images, identifies failure modes, and proposes recovery actions by selecting skills and updating goals. In simulation, our method achieved an 83% success rate, recovering from failures induced by randomized conditions, including grasp misalignments up to 5 degrees, hole-pose errors up to 20 mm, fivefold increases in friction, and unseen square/rectangular pegs, and we further validated the approach on a real robot. Project page is available at https://omron-sinicx.github.io/compliance-enabled-failure-recovery/.
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