Learning Contact Dynamics for Control with Action-conditioned Face Interaction Graph Networks

• URL: http://arxiv.org/abs/2509.12151v1
• Date: Mon, 15 Sep 2025 17:15:31 GMT
• Title: Learning Contact Dynamics for Control with Action-conditioned Face Interaction Graph Networks
• Authors: Zongyao Yi, Joachim Hertzberg, Martin Atzmueller,
• Abstract summary: We present a learnable physics simulator that provides accurate motion and force-torque prediction of robot end effectors in contact-rich manipulation.<n>The proposed model extends the state-of-the-art GNN-based simulator with novel node and edge types.
• Score: 4.942750934906048
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a learnable physics simulator that provides accurate motion and force-torque prediction of robot end effectors in contact-rich manipulation. The proposed model extends the state-of-the-art GNN-based simulator (FIGNet) with novel node and edge types, enabling action-conditional predictions for control and state estimation tasks. In simulation, the MPC agent using our model matches the performance of the same controller with the ground truth dynamics model in a challenging peg-in-hole task, while in the real-world experiment, our model achieves a 50% improvement in motion prediction accuracy and 3$\times$ increase in force-torque prediction precision over the baseline physics simulator. Source code and data are publicly available.

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