PhyPush is a physics-guided Transformer that estimates mass and friction from single-push velocity data, claiming over 10% error reduction versus privileged-force baselines in simulation and successful sim-to-real zero-shot transfer.
Phys2real: Fusing vlm priors with interactive online adapta- tion for uncertainty-aware sim-to-real manipulation,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
The paper proposes an Adaptive Safety Architecture with a mutual-information-based Compound Uncertainty Coefficient, MaxInfoRL policies, and adaptive constraints to actively resolve compound epistemic uncertainty in RL rather than passively enduring it.
A simulator-in-the-loop multi-modal method refines physical properties of incomplete 3D articulated objects to improve simulation stability and downstream robot policy performance.
citing papers explorer
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PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
PhyPush is a physics-guided Transformer that estimates mass and friction from single-push velocity data, claiming over 10% error reduction versus privileged-force baselines in simulation and successful sim-to-real zero-shot transfer.
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Robots Need More than VLA and World Models
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
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Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty
The paper proposes an Adaptive Safety Architecture with a mutual-information-based Compound Uncertainty Coefficient, MaxInfoRL policies, and adaptive constraints to actively resolve compound epistemic uncertainty in RL rather than passively enduring it.
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Automatically Improving Simulation Physics for Articulated Objects
A simulator-in-the-loop multi-modal method refines physical properties of incomplete 3D articulated objects to improve simulation stability and downstream robot policy performance.