PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers

• URL: http://arxiv.org/abs/2605.26284v1
• Date: Mon, 25 May 2026 19:14:43 GMT
• Title: PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
• Abstract summary: This paper presents PhyPush, a physics-guided Transformer framework that estimates an object's mass from a single push.<n>In simulation, it reduces error by over 10% compared with a baseline that has privileged access to full force information.<n>Results demonstrate that physics-guided learning can enable low-cost, sensor-efficient estimation of physical properties.
• Score: 2.032261439690125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately estimating object mass and friction is fundamental to achieving reliable and adaptive robotic manipulation. Although interactive perception provides a powerful mechanism for inferring such properties, most existing approaches depend on specialized hardware such as force/torque sensors, tactile arrays, or multi-camera motion-capture systems, limiting scalability and deployment. This paper presents PhyPush, a physics-guided Transformer framework that estimates an object's mass and friction coefficient using only kinematically derived end-effector velocity from a single push. This typically requires data available on standard robotic arms. The model incorporates constraints from Newton's second law and the Coulomb friction model through a physics-guided loss, improving physical consistency and generalization to unseen objects and surfaces. Across diverse simulation and real-world setups, PhyPush consistently achieves more accurate mass and friction estimation in challenging out-of-domain conditions. In simulation, it reduces error by over 10% compared with a baseline that has privileged access to full force information, while in real-world experiments, it outperforms a data-driven loss approach. Overall, the results demonstrate that physics-guided learning can enable low-cost, sensor-efficient estimation of physical properties, relying solely on a single push and readily available kinematic data.

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