Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery
Mimo Shirasaka, Cristian Camilo Beltran-Hernandez, Masashi Hamaya, Yoshitaka Ushiku
AI summary
Problem
Object insertion tasks are highly sensitive to pose uncertainty and environmental variations, typically requiring precise force sensing, rigid fixturing, or manual controller retraining to handle failures.
Approach
The method structures insertion into sequential compliance-enabled contact formations and uses a pre-trained VLM to evaluate terminal states and autonomously generate recovery plans when failures occur.
Key results
- 83% simulation success rate under randomized uncertainties
- Recovery from 5° grasp misalignments and 20 mm hole-pose errors
- Adaptation to 5x friction increases and unseen peg geometries
- Successful real-robot validation of compliance-enabled recovery
Why it matters
Demonstrates how passive compliance and foundation models can replace complex force sensing and retraining for reliable robotic assembly in unstructured settings.
Abstract
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 in- sight 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◦, hole-pose errors up to 20 mm, fivefold increases in friction, and unseen square/rectangular pegs, and we further validate the approach on a real robot. Project page is available at https://omron-sinicx.github.io/ compliance-enabled-failure-recovery/.