A Model-based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers

TL;DR

This paper presents a model-based visual force sensing system for soft robotic grippers that uses RGB-D images and iterative contact localization to estimate grasp forces accurately and robustly.

Contribution

It introduces a novel approach combining deep learning and finite element analysis for indirect force estimation adaptable to unseen objects and scenarios.

Findings

Achieved an average RMS error of 0.23 N during load phase.

Demonstrated robustness to occlusion and object variability.

Showcased potential for real-time force sensing in soft robotics.

Abstract

Grasp force estimation can help prevent robots from damaging delicate objects during manipulation and improve learning-based robotic control. Integrating force sensing into deformable grippers negotiates trade-offs in cost, complexity, mechanical robustness, and performance. With the growing integration of RGB-D wrist cameras into robotic systems for control purposes, camera-based techniques are a promising solution for indirect visual force estimation. Current approaches mostly utilize end-to-end deep learning, which can be brittle when generalizing to new scenarios, while existing model-based approaches are unsuited to grasping and modern grasper geometries. To address these challenges, we developed a model-based visual force sensing approach integrating an iterative contact localization with generalization to unseen objects. The system extracts structural key points from wrist camera RGB-D images of deforming fin-ray-shaped soft grippers, and uses these key points to define parameters of an inverse finite element analysis simulation in Simulation Open Framework Architecture. The iterative contact localization sub-system utilizes a deep learning-based online 3D reconstruction and pose estimation pipeline to dynamically update contact location, and is robust to visual occlusion and unseen objects. Our system demonstrated an average root mean square error of 0.23 N and normalized root mean square deviation of 2.11% during the load phase, and 0.48 N and 4.34% over the entire grasping process when interacting with different objects under various conditions, showcasing its potential for real-time model-based indirect force sensing of soft grippers.