Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery

Abstract-- Object insertion tasks are prone to failures under pose uncertainties and environmental variations, traditionally requiring manual finetuning 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. I. INTRODUCTION Peg-in-hole tasks have been widely studied but remain challenging due to their contact-rich nature and tight tolerances [1]. A central difficulty lies in handling uncertainties in part grasping and hole pose. Conventional methods address these uncertainties through precise pose estimation and rigid fixturing, enabling repetitive pick-and-place operations [2], but such approaches demand substantial engineering effort.