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REVIEW 2 major objections 2 minor 20 references

A physics-guided Transformer estimates mass and friction from one velocity-only push by embedding Newton's laws in its loss.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-07-02 23:06 UTC pith:QUA7UZOM

load-bearing objection PhyPush claims single-push velocity-only estimation via physics-guided Transformer but the under-determination concern from the stress-test looks real and unresolved in the abstract. the 2 major comments →

arxiv 2605.26284 v2 pith:QUA7UZOM submitted 2026-05-25 cs.RO

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

Koyo Fujii , Luis Figueredo , Praminda Caleb-Solly , Ivan Boschi , Edoardo Ida' , Marco Carricato , Aly Magassouba This is my paper
classification cs.RO
keywords physical property estimationphysics-guided learningtransformerrobotic manipulationsensorless estimationmass estimationfriction estimationsim-to-real transfer
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that mass and friction coefficients can be recovered accurately from end-effector velocity recorded during a single push. It does so by training a Transformer whose loss penalizes violations of Newton's second law and the Coulomb friction model, removing the need for force or torque sensors. A reader should care because this removes a hardware barrier that currently limits interactive perception to specialized robots. The result is a method that works on ordinary arms, generalizes to objects and surfaces never seen in training, and transfers directly from simulation to physical experiments.

Core claim

PhyPush is a physics-guided Transformer that estimates an object's mass and friction coefficient using only end-effector velocity from a single push. By incorporating Newton's second law and the Coulomb friction model through a physics-guided loss, the model improves physical consistency and generalizes to unseen objects and surfaces. Across diverse setups, PhyPush consistently achieves highly accurate estimations in challenging out-of-domain conditions. In simulation, it reduces error by over 10% compared to a baseline with privileged force data, while in real-world experiments, it successfully zero-shot transfers from simulation to outperform a purely data-driven baseline.

What carries the argument

The physics-guided loss that enforces consistency with Newton's second law and the Coulomb friction model while training the Transformer on velocity sequences.

Load-bearing premise

Velocity data alone, combined with the standard Newtonian and Coulomb models inside the loss, is sufficient to recover accurate mass and friction values without force measurements or additional sensing.

What would settle it

Collect velocity traces from pushes on a new surface whose friction deviates from the Coulomb model; if the model's mass and friction estimates produce large prediction errors when the equations of motion are integrated forward, the central claim is falsified.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Standard robotic arms without force sensors can still obtain reliable mass and friction estimates from one push.
  • Estimation accuracy exceeds that of models given privileged force data in simulation.
  • Models trained only in simulation produce usable estimates on real robots without fine-tuning.
  • Performance remains high on objects and surfaces absent from the training distribution.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Robots could use the estimates to adjust grasp forces or push trajectories on the fly without extra hardware.
  • The same loss-construction pattern might let velocity-only data support estimation of additional inertial parameters.
  • The method highlights how physics constraints can substitute for missing sensor channels in other manipulation tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper introduces PhyPush, a physics-guided Transformer that estimates object mass and friction coefficient from a single push using only end-effector velocity data available on standard robotic arms. Newton's second law and the Coulomb friction model are incorporated via a physics-guided loss to enforce physical consistency and improve generalization to unseen objects and surfaces. The work reports over 10% error reduction versus a privileged-force baseline in simulation and successful zero-shot sim-to-real transfer that outperforms a purely data-driven baseline in real experiments.

Significance. If the central estimation result holds and the degeneracy concern is resolved, the approach would be significant for scalable sensorless manipulation, removing the need for force/torque sensors on commodity arms. The combination of a single-push protocol with physics-guided loss for out-of-domain generalization and zero-shot transfer would be a useful contribution if the identifiability of m and μ from velocity alone is demonstrated.

major comments (2)
  1. [Method / physics-guided loss] Method section (physics loss formulation): The central claim requires that velocity trajectories alone, under a physics loss enforcing F=ma and Coulomb friction, uniquely recover both m and μ. Because contact force is never measured and the robot is presumably velocity-controlled, the applied force is a reaction that depends on the unknown parameters. This creates a potential degeneracy in which multiple (m, μ) pairs produce indistinguishable velocity profiles. The manuscript must supply the explicit loss equation, weighting scheme, and either an identifiability analysis or empirical evidence (e.g., synthetic ablation showing unique recovery) that resolves this circularity.
  2. [Experiments / simulation] Simulation results (comparison to privileged-force baseline): The claim that PhyPush reduces error by over 10% relative to a baseline given privileged force data is load-bearing for the sensorless advantage. The paper should specify the exact baseline architecture, loss, and input features, and report per-object error distributions with standard deviations or confidence intervals to substantiate the improvement.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'highly accurate estimations' is vague; replace with concrete metrics (mean absolute error, relative error, etc.) for mass and friction.
  2. [Method] Notation: Define all symbols (e.g., how the physics loss term is written) at first use rather than assuming familiarity with the Transformer architecture details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments on our manuscript. We address each of the major comments point by point below. We believe the concerns can be addressed through clarifications and additional details in the revised version.

read point-by-point responses

  1. Referee: [Method / physics-guided loss] Method section (physics loss formulation): The central claim requires that velocity trajectories alone, under a physics loss enforcing F=ma and Coulomb friction, uniquely recover both m and μ. Because contact force is never measured and the robot is presumably velocity-controlled, the applied force is a reaction that depends on the unknown parameters. This creates a potential degeneracy in which multiple (m, μ) pairs produce indistinguishable velocity profiles. The manuscript must supply the explicit loss equation, weighting scheme, and either an identifiability analysis or empirical evidence (e.g., synthetic ablation showing unique recovery) that resolves this circularity.

    Authors: We acknowledge the importance of demonstrating identifiability. In the revised manuscript, we will include the explicit formulation of the physics-guided loss, including the weighting scheme between data loss and physics loss terms. Additionally, we will provide an identifiability analysis based on the model dynamics and empirical evidence from synthetic experiments showing that the optimization converges to unique (m, μ) pairs for given velocity trajectories under the assumed physics model. revision: yes

  2. Referee: [Experiments / simulation] Simulation results (comparison to privileged-force baseline): The claim that PhyPush reduces error by over 10% relative to a baseline given privileged force data is load-bearing for the sensorless advantage. The paper should specify the exact baseline architecture, loss, and input features, and report per-object error distributions with standard deviations or confidence intervals to substantiate the improvement.

    Authors: We agree that more details are needed to substantiate the comparison. The privileged-force baseline uses the same Transformer architecture but takes force measurements as additional input features. The loss is the standard MSE on the parameter predictions. We will expand the manuscript to detail the baseline architecture, loss function, and input features. Furthermore, we will include per-object error distributions along with standard deviations and confidence intervals in the revised results section. revision: yes

Circularity Check

0 steps flagged

No circularity: standard external physics models used in loss without self-definition or fitted predictions

full rationale

The paper's central approach incorporates Newton's second law and the Coulomb friction model via a physics-guided loss to estimate mass and friction from velocity data. These are established external physical laws, not derived from or defined in terms of the model's outputs. No self-citations, ansatzes, or uniqueness theorems from prior author work are invoked in the provided text to justify the formulation. The estimation process is presented as using these independent models to enforce consistency, with no reduction of predictions to fitted inputs by construction. This is a normal non-finding for a paper relying on textbook mechanics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, invented entities, or additional axioms beyond the standard physics models named in the loss.

axioms (1)
  • domain assumption Newton's second law and the Coulomb friction model accurately represent the contact dynamics during a push
    Invoked directly in the physics-guided loss term

pith-pipeline@v0.9.1-grok · 5699 in / 1219 out tokens · 24007 ms · 2026-07-02T23:06:20.277452+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers." pith.science (2026). https://pith.science/paper/QUA7UZOM

@misc{pith2026260526284,
  author       = {Pith},
  title        = {Pith review of: PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QUA7UZOM}},
  note         = {Machine review of arXiv:2605.26284}
}
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read the original abstract

Accurately estimating object mass and friction is fundamental to reliable robotic manipulation. While interactive perception is powerful, most approaches rely on specialized hardware like force/torque sensors, limiting scalability. This paper introduces PhyPush, a physics-guided Transformer that estimates an object's mass and friction coefficient using only end-effector velocity from a single push, data readily available on standard robotic arms. By incorporating Newton's second law and the Coulomb friction model through a physics-guided loss, the model improves physical consistency and generalizes to unseen objects and surfaces. Across diverse setups, PhyPush consistently achieves highly accurate estimations in challenging out-of-domain conditions. In simulation, it reduces error by over 10% compared to a baseline with privileged force data, while in real-world experiments, it successfully zero-shot transfers from simulation to outperform a purely data-driven baseline.

Figures

Figures reproduced from arXiv: 2605.26284 by Aly Magassouba, Edoardo Ida', Ivan Boschi, Koyo Fujii, Luis Figueredo, Marco Carricato, Praminda Caleb-Solly.

Figure 1
Figure 1. Figure 1: One push is all PhyPush needs: from a single translational interaction, our physics-guided transformer framework predicts an object’s mass and friction coefficient using only the end-effector velocity profile, avoiding specialized sensing hardware such as force/torque sensors, tactile sensing, and external motion-capture or camera systems. are costly, difficult to maintain, and often impractical outside co… view at source ↗
Figure 2
Figure 2. Figure 2: PhyPush system pipeline: The core intuition behind our PhyPush is that an object with distinct properties exhibits unique behavioral responses to the same push action. Therefore, our framework estimates these properties by observing the resulting object dynamics. Specifically, the robot applies a single translational push where the end-effector maintains continuous contact with the object, allowing the end… view at source ↗
Figure 3
Figure 3. Figure 3: PhyPush was validated experimentally across objects and surfaces with diverse physical properties. (*) indicates objects that also appear in the training set. of our curriculum-style annealing and the deep coupling of mb and µb in the unsupervised loss Lforce, acc un. E. Real-world Results We conducted an extensive experimental study across three different surfaces and a range of objects with varying weigh… view at source ↗
Figure 4
Figure 4. Figure 4: Real-world performance of PhyPush on seen objects. Each row corresponds to one of the 33 evaluated object–surface–conditions (plastic cube with rubber, wood rough, and smooth surfaces), and the three columns report mass, friction coefficient, and the derived friction force Ffric = ˆµmg ˆ . The radar vertices denote the different ground-truth conditions [m, µ, Ffric], while the curves compare the ground tru… view at source ↗
Figure 5
Figure 5. Figure 5: Real-world performance of PhyPush on unseen objects across 14 object–surface–condition combinations, including the plastic container, tin can, and wooden box. The three columns report mass, friction coefficient, and the derived friction force Ffric = ˆµmg ˆ . Each radar vertex corresponds to one unseen test condition, identified by the object, contact surface, and ground-truth tuple [m, µ, Ffric]. The plot… view at source ↗

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Reference graph

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