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Robotic grippers use AI-powered visual sensing for precise force estimation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel model-based system for estimating grasp forces in robotic grippers using visual feedback from RGB-D cameras. This approach integrates iterative contact localization with an inverse finite element analysis simulation, allowing it to generalize to unseen objects and conditions. The system demonstrated high accuracy, with an average root mean square error of 0.23 N during the load phase and 4.34% over the entire grasping process. AI

IMPACT Improves robotic manipulation safety and control by enabling accurate indirect force sensing.

RANK_REASON Academic paper detailing a new system for robotic grippers.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Robotic grippers use AI-powered visual sensing for precise force estimation

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Kaiwen Zuo, Shuyuan Yang, Zonghe Chua ·

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

    arXiv:2605.00307v1 Announce Type: cross 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, comple…

  2. arXiv cs.CV TIER_1 English(EN) · Zonghe Chua ·

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

    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…

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