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Robotic peg insertion enhanced by learned contact dynamics model

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel graph neural network model capable of learning contact dynamics for robotic manipulation tasks like peg insertion. This model, trained through self-supervision using only touch and force-torque data, can predict object pose updates and reaction forces. When integrated with a model predictive control agent, it achieved a 98% success rate in simulation for peg insertion with unseen geometries and significantly outperformed a system-identified MuJoCo model in real-world tests for position, force, and torque accuracy. AI

IMPACT Enhances robotic manipulation capabilities, potentially leading to more precise and adaptable automation in manufacturing and assembly.

RANK_REASON Academic paper detailing a new method for robotic manipulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Robotic peg insertion enhanced by learned contact dynamics model

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Academic paper detailing a new method for robotic manipulation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zongyao Yi, Joachim Hertzberg, Martin Atzmueller ·

    Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion

    arXiv:2509.12151v3 Announce Type: replace-cross Abstract: We present a learnable physics-based model that predicts motion of the robot end effector and reaction force-torque in contact-rich manipulation. The model represents the end effector and the environment as interacting mes…

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