Physical Reasoning

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7 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

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Automatically Improving Simulation Physics for Articulated Objects
May 18, 2026Anh-Quan PhamPhysical Reasoning Robotics Simulation
University of Pennsylvania
Do Vision-Language-Models show human-like logical problem-solving capability in point and click puzzle games?
May 11, 2026Maximilian Triebel, Marco Menner, Dominik HelfensteinVLM Evaluation Physical Reasoning
Institute of Artificial Intelligence, University of Stuttgart, Stuttgart, Germany.
SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning
May 10, 2026Kun Xiang, Terry Jingchen Zhang, Zirong Liu +15Cross-Modal Learning Multimodal Grounding
Sun Yat-sen University · ETH Zurich · Huawei Technologies Ltd. +1
NICE FACT: Diagnosing and Calibrating VLMs in Quantitative Reasoning for Kinematic Physics
May 8, 2026Jian Lan, Zhicheng Liu, Xinpeng Wang +5VLM Evaluation Physical Reasoning
University of Munich (LMU), Germany 2Munich Center of Machine Learning3 Sichuan University
StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
May 5, 2026Xiaowen Sun, Matthias Kerzel, Mengdi Li +3Physical Reasoning VLM Adaptation
Department of Informatics, University of Hamburg, Germany · King Abdullah University of Science and Technology, Saudi Arabia
Investigating LLM's Problem Solving Capability -- a Study on Statics Questions
Apr 30, 2026Tanner Culleton, Hung-Fu ChangLLM Evaluation Physical Reasoning
1R. B, Annis School of Engineering, University of Indianapolis, Indianapolis, IN, 46227, USA
Fine-Tuning Small Reasoning Models for Quantum Field Theory
Apr 21, 2026Nathaniel S. Woodward, Zhiqi Gao, Yurii Kvasiuk +3Physical Reasoning LLM Fine-Tuning
Department of Physics, University of Wisconsin-Madison · Department of Computer Science, University of Wisconsin-Madison · Perimeter Institute for Theoretical Physics
TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
Mar 22, 2026Feng Liu, Xin Cui, Jian Xu +11Physical Reasoning AI Agents for Scientific Discovery
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China. +3
Modeling The Object Representations Underlying Human Physical Reasoning
Feb 12, 2026Andrey Gizdov, Andrea Procopio, Lorenzo Caputi +4Physical Reasoning Computational Cognitive Modeling
Harvard University · Weizmann Institute of Science · Bocconi University
PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding
Nov 25, 2025Haoze Zhang, Tianyu Huang, Zichen Wan +4Physical Consistency in Video Generation Physical Reasoning
1Harbin Institute of Technology, China

May 18, 2026, cs.ROJ/K move · Enter open · S save

Automatically Improving Simulation Physics for Articulated Objects

Anh-Quan Pham

University of Pennsylvania

Physical Reasoning Robotics Simulation Articulated Object Manipulation

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Simulation is a central tool for scalable robot learning, but its effectiveness depends on the quality of object assets. While modern 3D datasets provide rich geometric and kinematic representations, they typically lack the physical properties required for stable and realistic interaction, requiring significant manual effort to construct simulation-ready articulated objects. In this thesis, we introduce interaction-readiness, which characterizes whether an object can be reliably simulated under manipulation. We propose a quantitative evaluation framework that decomposes interaction-readiness into measurable components, enabling systematic analysis of object quality and revealing failure modes not captured by conventional evaluation. We further present a multi-modal, simulator-in-the-loop approach for generating interaction-ready articulated objects from incomplete 3D assets. The method integrates geometric, visual, and semantic information to infer physical properties and refines them through iterative simulator feedback to improve physical consistency. Experiments across diverse articulated objects and manipulation tasks show that object quality directly impacts simulation stability, interaction behavior, and policy performance. Objects refined by our method exhibit more stable and realistic dynamics, enabling more reliable downstream learning and evaluation. Overall, this thesis demonstrates the importance of physical realism for articulated objects in simulation and introduces a practical multi-modal refinement approach, guided by simulator feedback, for constructing such objects at scale.

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