PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
This paper introduces PhyPush, a physics-guided Transformer framework that estimates an object's mass and friction coefficient using only end-effector velocity from a single push, eliminating the need for force/torque sensors. Experiments show reduced error in simulation and real-world settings.
Article intelligence
Key points
- Estimates mass and friction from a single push using kinematic data
- Incorporates Newton's second law and Coulomb friction via physics-guided loss
- Achieves over 10% error reduction in simulation compared to force-sensor baseline
- Outperforms data-driven methods in real-world tests
Why it matters
This matters because estimates mass and friction from a single push using kinematic data.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26284] PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
[Submitted on 25 May 2026]
Title:PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
View a PDF of the paper titled PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers, by Koyo Fujii and Luis Figueredo and Praminda Caleb-Solly and Ivan Boschi and Edoardo Ida' and Marco Carricato and Aly Magassouba
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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.
Comments: Submitted to 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.26284 [cs.RO]
(or arXiv:2605.26284v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.26284
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Koyo Fujii [view email] [v1] Mon, 25 May 2026 19:14:43 UTC (4,714 KB)
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