pith. sign in

arxiv: 2605.19136 · v1 · pith:SGHJLYCXnew · submitted 2026-05-18 · 💻 cs.RO

Automatically Improving Simulation Physics for Articulated Objects

Anh-Quan Pham This is my paper

Pith reviewed 2026-05-20 08:56 UTC · model grok-4.3

classification 💻 cs.RO
keywords simulation physicsarticulated objectsrobot learninginteraction readinessphysical property inferencesimulator feedback3D asset refinementmanipulation stability
0
0 comments X

The pith

A simulator-in-the-loop method infers and corrects physical properties of articulated objects from incomplete 3D assets to improve simulation stability.

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

The paper establishes that modern 3D datasets supply geometry and kinematics for articulated objects but omit the physical properties needed for stable robotic manipulation in simulation. It defines interaction-readiness as the property that lets an object behave reliably under contact and proposes a multi-modal refinement process that combines geometric, visual, and semantic cues with repeated simulator feedback. Experiments demonstrate that objects processed this way produce steadier dynamics, fewer simulation failures, and stronger performance when used to train or evaluate manipulation policies. The work therefore argues that closing the gap between available 3D models and simulation-ready assets can be done automatically rather than through manual tuning.

Core claim

The central claim is that a multi-modal, simulator-in-the-loop refinement procedure can automatically infer and adjust missing physical parameters of articulated objects so that the resulting assets satisfy measurable interaction-readiness criteria and exhibit more stable, realistic contact behavior during manipulation tasks.

What carries the argument

The multi-modal simulator-in-the-loop refinement loop that ingests geometric, visual, and semantic cues and iteratively updates physical parameters until simulator feedback indicates improved consistency.

If this is right

  • Higher-quality object assets directly reduce simulation crashes and erratic contact forces during manipulation.
  • Policies trained or evaluated on refined objects transfer more reliably to new tasks.
  • The same refinement loop can be applied to large existing 3D datasets to produce libraries of interaction-ready articulated objects.
  • Evaluation frameworks that measure interaction-readiness components expose failure modes invisible to standard geometric or kinematic checks.

Where Pith is reading between the lines

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

  • The method could be extended to non-articulated rigid bodies or deformable objects by re-using the same cue-and-feedback structure.
  • If the refinement generalizes across simulators, it offers a route to standardize physical properties for cross-simulator robot learning benchmarks.
  • Repeated application over many objects may reveal statistical patterns in typical missing parameters that could inform future dataset design.

Load-bearing premise

Iterative simulator feedback together with geometric, visual, and semantic information is sufficient to recover accurate physical properties without any ground-truth measurements.

What would settle it

Run the same manipulation policies on both original and refined object sets in an independent simulator or on a real robot and observe whether the refined set still produces measurably fewer instability events or policy failures.

Figures

Figures reproduced from arXiv: 2605.19136 by Anh-Quan Pham.

Figure 4
Figure 4. Figure 4: FIGURE 4.1 Asset2Sim is a simulator-in-the-loop interaction-ready asset creation pipeline [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIGURE 5.1 Failure mode distribution across all 93 assets spanning 10 object categories. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 4.1
Figure 4.1. Figure 4.1: Asset2Sim is a simulator-in-the-loop interaction-ready asset creation pipeline with three [PITH_FULL_IMAGE:figures/full_fig_p037_4_1.png] view at source ↗
Figure 5.1
Figure 5.1. Figure 5.1: Failure mode distribution across all 93 assets spanning 10 object categories. Each bar [PITH_FULL_IMAGE:figures/full_fig_p059_5_1.png] view at source ↗
Figure 5.2
Figure 5.2. Figure 5.2: Representative frames (ordered by time) from VLA trajectories in IsaacLab simulations [PITH_FULL_IMAGE:figures/full_fig_p060_5_2.png] view at source ↗
Figure 5.3
Figure 5.3. Figure 5.3: Representative frames (ordered by time) from VLA trajectories in IsaacLab simulations [PITH_FULL_IMAGE:figures/full_fig_p061_5_3.png] view at source ↗
Figure 5.4
Figure 5.4. Figure 5.4: Representative frames (ordered by time) from VLA trajectories in IsaacLab simulations [PITH_FULL_IMAGE:figures/full_fig_p062_5_4.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the concept of interaction-readiness for articulated objects in simulation and presents a multi-modal, simulator-in-the-loop refinement method that combines geometric, visual, and semantic cues to infer and iteratively correct physical properties (such as mass, friction, and joint parameters) from incomplete 3D assets. It also proposes a quantitative evaluation framework decomposing interaction-readiness into measurable components. Experiments on diverse objects and manipulation tasks are claimed to demonstrate that refined objects yield improved simulation stability, interaction behavior, and downstream policy performance compared to unrefined assets.

Significance. If the refinements can be shown to produce dynamics that better match real-world behavior rather than merely stabilizing the simulator, the approach would address a significant practical barrier in scalable robot learning by reducing the manual effort needed to create simulation-ready articulated assets from existing 3D datasets.

major comments (2)
  1. Experiments section: the central claim that refined objects exhibit 'more stable and realistic dynamics' enabling better policy performance rests entirely on metrics collected inside the identical simulator supplying the iterative feedback signal. No real-world measurements, external ground-truth data, or cross-simulator validation are described, leaving open the possibility that parameters are tuned only to eliminate simulator-specific artifacts rather than achieving physical fidelity.
  2. Method overview (and Abstract): the inference of physical properties from geometric/visual/semantic cues and the precise form of the simulator feedback loop (including any objective function, parameter update rule, or stopping criterion) are not specified with sufficient detail to assess reproducibility or to determine whether the process is parameter-free or relies on hand-tuned thresholds.
minor comments (2)
  1. The abstract refers to the work as 'this thesis,' which should be revised for consistency with journal article conventions.
  2. Clarify the exact set of physical parameters being refined and how they are initialized from the input 3D assets.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive and detailed review of our manuscript. Below we address each of the major comments point by point, indicating where we agree and the revisions we plan to implement.

read point-by-point responses

  1. Referee: Experiments section: the central claim that refined objects exhibit 'more stable and realistic dynamics' enabling better policy performance rests entirely on metrics collected inside the identical simulator supplying the iterative feedback signal. No real-world measurements, external ground-truth data, or cross-simulator validation are described, leaving open the possibility that parameters are tuned only to eliminate simulator-specific artifacts rather than achieving physical fidelity.

    Authors: We agree with the referee that our evaluation is performed within the same simulator used for the refinement process, which could potentially optimize for simulator-specific behaviors. Our primary contribution is demonstrating that the proposed refinement leads to more stable simulations and better policy performance in manipulation tasks, as measured by our interaction-readiness framework. To address this concern, we will revise the manuscript to tone down claims of 'realistic dynamics' and instead focus on 'improved simulation stability and interaction behavior'. We will also include a new subsection in the discussion that explicitly acknowledges the lack of real-world or cross-simulator validation and outlines plans for such evaluations in future work. revision: yes

  2. Referee: Method overview (and Abstract): the inference of physical properties from geometric/visual/semantic cues and the precise form of the simulator feedback loop (including any objective function, parameter update rule, or stopping criterion) are not specified with sufficient detail to assess reproducibility or to determine whether the process is parameter-free or relies on hand-tuned thresholds.

    Authors: We appreciate this feedback on the clarity of our method description. While the full manuscript provides additional details beyond the abstract, we recognize that the overview may not be sufficient for reproducibility. In the revised manuscript, we will enhance the method section by providing a more precise specification of how geometric, visual, and semantic cues are combined to infer initial physical properties. We will also detail the simulator feedback loop, including the objective function that minimizes instability metrics, the parameter update rule based on iterative search, and the stopping criterion when changes in key metrics fall below a threshold. We will explicitly state the hand-tuned parameters and their values to allow readers to reproduce the process. revision: yes

standing simulated objections not resolved
  • Conducting real-world measurements or cross-simulator validation to directly verify physical fidelity, as this is beyond the scope of the current simulation-focused study and would require substantial new resources.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper defines interaction-readiness as a decomposable property and presents a multi-modal simulator-in-the-loop refinement procedure that incorporates geometric, visual, semantic cues plus iterative feedback to infer and adjust physical parameters. Experiments then measure downstream effects on stability, interaction behavior, and policy performance inside simulation. No equations, fitted parameters, or self-citations appear in the provided text that reduce any claimed prediction or result to the refinement inputs by construction; the evaluation framework supplies independent measurable components against which improvements are reported. The derivation therefore remains self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated in the text.

pith-pipeline@v0.9.0 · 5736 in / 1089 out tokens · 45891 ms · 2026-05-20T08:56:34.620061+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 6 internal anchors

  1. [1]

    URL https://arxiv.org/abs/1512. 03012. Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Lud- wig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects.arXiv preprint arXiv:2212.08051,

    work page arXiv

  2. [2]

    Gapartnet: Cross- category domain-generalizable object perception and manip- ulation via generalizable and actionable parts.arXiv preprint arXiv:2211.05272, 2022

    91 HaoranGeng, HelinXu, ChengyangZhao, ChaoXu, LiYi, SiyuanHuang, andHeWang. Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts.arXiv preprint arXiv:2211.05272,

    work page arXiv

  3. [3]

    URL https: //arxiv.org/abs/2506.04941. Abhishek Joshi, Beining Han, Jack Nugent, Max Gonzalez Saez-Diez, Yiming Zuo, Jonathan Liu, Hongyu Wen, Stamatis Alexandropoulos, Karhan Kayan, Anna Calveri, Tao Sun, Gaowen Liu, Yi Shao, Alexander Raistrick, and Jia Deng. Procedural generation of articulated simulation- ready assets,

    work page arXiv

  4. [4]

    Procedural genera- tion of articulated simulation-ready assets, 2025

    URL https://arxiv.org/abs/2505.10755. Rishabh Kabra, Loic Matthey, Alexander Lerchner, and Niloy J Mitra. Leveraging vlm-based pipelines to annotate 3d objects.arXiv preprint arXiv:2311.17851,

    work page arXiv

  5. [5]

    Articulate-anything: Automatic modeling of articulated objects via a vision-language foundation model.arXiv preprint arXiv:2410.13882, 2024

    Long Le, Jason Xie, William Liang, Hung-Ju Wang, Yue Yang, Yecheng Jason Ma, Kyle Vedder, Arjun Krishna, Dinesh Jayaraman, and Eric Eaton. Articulate-anything: Automatic modeling of articulated objects via a vision-language foundation model.arXiv preprint arXiv:2410.13882,

    work page arXiv

  6. [6]

    Pixie: Fast and generalizable supervised learning of 3d physics from pixels.arXiv preprint arXiv:2508.17437, 2025

    Long Le, Ryan Lucas, Chen Wang, Chuhao Chen, Dinesh Jayaraman, Eric Eaton, and Lingjie Liu. Pixie: Fast and generalizable supervised learning of 3d physics from pixels.arXiv preprint arXiv:2508.17437,

    work page arXiv

  7. [7]

    Urdf-anything: Constructing articulated objects with 3d multimodal language model.arXiv preprint arXiv:2511.00940, 2025

    Zhe Li, Xiang Bai, Jieyu Zhang, Zhuangzhe Wu, Che Xu, Ying Li, Chengkai Hou, and Shanghang Zhang. Urdf-anything: Constructing articulated objects with 3d multimodal language model. arXiv preprint arXiv:2511.00940,

    work page arXiv

  8. [8]

    LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

    Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning.arXiv preprint arXiv:2306.03310,

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning

    Mayank Mittal, Pascal Roth, James Tigue, Antoine Richard, Octi Zhang, Peter Du, Antonio Serrano-Muñoz, Xinjie Yao, René Zurbrügg, Nikita Rudin, Lukasz Wawrzyniak, Milad Rakhsha, Alain Denzler, Eric Heiden, Ales Borovicka, Ossama Ahmed, Iretiayo Akinola, Abrar Anwar, Mark T. Carlson, Ji Yuan Feng, Animesh Garg, Renato Gasoto, Lionel Gulich, Yijie Guo, M. G...

    work page internal anchor Pith review Pith/arXiv arXiv

  10. [10]

    Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning

    doi: 10.48550/arXiv.2511.04831. URL 93 https://arxiv.org/abs/2511.04831. Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su. PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. InThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2511.04831

  11. [11]

    DreamFusion: Text-to-3D using 2D Diffusion

    Accessed: 2026-04-27. Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion.arXiv preprint arXiv:2209.14988,

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  12. [12]

    arXiv preprint arXiv:2502.02590 (2025)

    Xiaowen Qiu, Jincheng Yang, Yian Wang, Zhehuan Chen, Yufei Wang, Tsun-Hsuan Wang, Zhou Xian, and Chuang Gan. Articulate anymesh: Open-vocabulary 3d articulated objects modeling. arXiv preprint arXiv:2502.02590,

    work page arXiv

  13. [13]

    Proximal Policy Optimization Algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms.arXiv preprint arXiv:1707.06347,

    work page internal anchor Pith review Pith/arXiv arXiv

  14. [14]

    Mu- joco: A physics engine for model-based control

    doi: 10.1109/IROS.2012.6386109. Trimesh Authors. Trimesh,

    work page doi:10.1109/iros.2012.6386109 2012

  15. [15]

    Maggie Wang, Stephen Tian, Aiden Swann, Ola Shorinwa, Jiajun Wu, and Mac Schwager

    URL https://github.com/mikedh/trimesh. Maggie Wang, Stephen Tian, Aiden Swann, Ola Shorinwa, Jiajun Wu, and Mac Schwager. Phys2real: Fusing vlm priors with interactive online adaptation for uncertainty-aware sim-to- real manipulation.arXiv preprint arXiv:2510.11689,

    work page arXiv

  16. [16]

    RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

    URL https://arxiv.org/abs/2604.09860. Yu Yang, Zhilu Zhang, Xiang Zhang, Yihan Zeng, Hui Li, and Wangmeng Zuo. Physworld: From real videos to world models of deformable objects via physics-aware demonstration synthesis. arXiv preprint arXiv:2510.21447,

    work page internal anchor Pith review Pith/arXiv arXiv