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

Abstract

We address robust and resilient object insertion using a passively compliant soft wrist that permits large deformations and safely absorbs contacts, without high-frequency control or force sensing. To improve robustness, we structure the task as compliance-enabled contact formations: a sequence of contact states that progressively constrain specific degrees of freedom. While this segmentation mitigates moderate uncertainty, failures still occur under severe pose errors or environmental variations (e.g., friction changes, peg geometry), which traditionally require retuning goals or retraining controllers. To achieve both robustness and resilience, we therefore integrate compliance-enabled failure recovery into the contact-formation framework. Our key insight is that wrist compliance permits safe, repeated recovery attempts. A pre-trained vision-language model (VLM) 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 previously unseen square/rectangular pegs—and we further validate the approach on a real robot.