URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model

• URL: http://arxiv.org/abs/2511.00940v1
• Date: Sun, 02 Nov 2025 13:45:51 GMT
• Title: URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model
• Abstract summary: We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
• Score: 76.08429266631823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Constructing accurate digital twins of articulated objects is essential for robotic simulation training and embodied AI world model building, yet historically requires painstaking manual modeling or multi-stage pipelines. In this work, we propose \textbf{URDF-Anything}, an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM). URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction. It implements a specialized $[SEG]$ token mechanism that interacts directly with point cloud features, enabling fine-grained part-level segmentation while maintaining consistency with the kinematic parameter predictions. Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches regarding geometric segmentation (mIoU 17\% improvement), kinematic parameter prediction (average error reduction of 29\%), and physical executability (surpassing baselines by 50\%). Notably, our method exhibits excellent generalization ability, performing well even on objects outside the training set. This work provides an efficient solution for constructing digital twins for robotic simulation, significantly enhancing the sim-to-real transfer capability.

Related papers

• GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning [15.060587346990792]
  We present GIF, an agentic Generation framework for Interactive and Functional object compositions.<n>GIF improves both asset quality and relation matching, while reducing collision rate to below 1%.<n>We synthesize data for policy learning, revealing diversity scaling in both simulation and real-world deployment.
  arXiv  Detail & Related papers  (2026-09-05T06:36:20Z)
• KineBench: Benchmarking Embodied World Models via IDM-Free Kinematic Grounding [58.541098216700796]
  evaluating the physical consistency of embodied world models is a critical open challenge.<n>Existing frameworks almost exclusively rely on Inverse Dynamics Models(IDMs) for action extraction.<n>We present KineBench, an IDM-free closed-loop benchmark for EWMs.
  arXiv  Detail & Related papers  (2026-07-22T08:04:17Z)
• Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation [62.51953630639423]
  Existing benchmarks lack visual realism, creating a large domain gap between simulation and reality.<n>We propose VISER, a visually realistic benchmark for evaluating robot manipulation in simulation.<n>VISER features a high-fidelity dataset of over 1,000 3D assets with physically-based rendering (PBR) materials, along with 3D scenes created from these assets through curated layouts or generation.
  arXiv  Detail & Related papers  (2026-05-07T14:13:05Z)
• Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation [63.01022057888141]
  Asset Harvester is an image-to-3D model and end-to-end pipeline that converts sparse, in-thewild object observations from real driving logs into complete, simulation-ready assets.<n>SparseViewDiT is explicitly designed to address limited-angle views and other real-world data challenges.
  arXiv  Detail & Related papers  (2026-04-20T16:20:57Z)
• URDF-Anything+: Autoregressive Articulated 3D Models Generation for Physical Simulation [45.4820195450296]
  We present an end-to-end autoregressive framework that directly generates executable articulated object models from visual observations.<n>Building on this capability, we enable a new Real-Follow-Sim paradigm, where high-fidelity digital twins constructed from visual observations allow policies trained in simulation to transfer to real robots without online adaptation.
  arXiv  Detail & Related papers  (2026-03-14T16:23:44Z)
• ArtLLM: Generating Articulated Assets via 3D LLM [19.814132638278547]
  ArtLLM is a novel framework for generating high-quality articulated assets directly from complete 3D meshes.<n>At its core is a 3D multimodal large language model trained on a large-scale articulation dataset.<n> Experiments show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction.
  arXiv  Detail & Related papers  (2026-03-01T15:07:46Z)
• ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning [31.000965640377128]
  ABot-M0 is a framework that builds a systematic data curation pipeline.<n>It enables end-to-end transformation of heterogeneous raw data into unified, efficient representations.<n>ABot-M0 supports modular perception via a dual-stream mechanism.
  arXiv  Detail & Related papers  (2026-02-11T16:47:01Z)
• Mirage2Matter: A Physically Grounded Gaussian World Model from Video [87.9732484393686]
  We present Simulate Anything, a graphics-driven world modeling and simulation framework.<n>Our approach reconstructs real-world environments into a photorealistic scene representation using 3D Gaussian Splatting (3DGS)<n>We then leverage generative models to recover a physically realistic representation and integrate it into a simulation environment via a precision calibration target.
  arXiv  Detail & Related papers  (2026-01-24T07:43:57Z)
• InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation [77.07565723756119]
  We present InternVLA-A1, a vision-language-action model with dynamic predictive capabilities.<n>We pre-train these models on heterogeneous data sources over real-world robot data, synthetic simulation data, and human videos.<n>We evaluate InternVLA-A1 on 12 real-world robotic tasks and a simulation benchmark.
  arXiv  Detail & Related papers  (2026-01-05T18:54:29Z)
• Visual Autoregressive Modelling for Monocular Depth Estimation [69.01449528371916]
  We propose a monocular depth estimation method based on visual autoregressive ( VAR) priors.<n>Our method adapts a large-scale text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism.<n>We report state-of-the-art performance in indoor benchmarks under constrained training conditions, and strong performance when applied to outdoor datasets.
  arXiv  Detail & Related papers  (2025-12-27T17:08:03Z)
• Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects [59.51185639557874]
  We introduce Kinematify, an automated framework that synthesizes articulated objects directly from arbitrary RGB images or textual descriptions.<n>Our method addresses two core challenges: (i) inferring kinematic topologies for high-DoF objects and (ii) estimating joint parameters from static geometry.
  arXiv  Detail & Related papers  (2025-11-03T07:21:42Z)
• Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation [0.6533458718563319]
  This paper presents a framework designed to enhance the grasping capabilities of quadrupeds equipped with arms.<n>We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects.<n>This dataset was used to train a custom CNN with a U-Net-like architecture that processes multi-modal input from an onboard RGB and depth cameras.<n>We validated the complete framework on a four-legged robot.
  arXiv  Detail & Related papers  (2025-08-24T17:47:56Z)
• RoboPearls: Editable Video Simulation for Robot Manipulation [81.18434338506621]
  RoboPearls is an editable video simulation framework for robotic manipulation.<n>Built on 3D Gaussian Splatting (3DGS), RoboPearls enables the construction of photo-realistic, view-consistent simulations.<n>We conduct extensive experiments on multiple datasets and scenes, including RLBench, COLOSSEUM, Ego4D, Open X-Embodiment, and a real-world robot.
  arXiv  Detail & Related papers  (2025-06-28T05:03:31Z)
• ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting [29.69981069695724]
  We tackle the challenge of concurrent reconstruction at the part level with the RGB appearance and estimation of motion parameters.<n>We reconstruct the articulated object in 3D Gaussian representations with both appearance and geometry information at the same time.<n>We introduce ArticulatedGS, a self-supervised, comprehensive framework that autonomously learns to model shapes and appearances at the part level.
  arXiv  Detail & Related papers  (2025-03-11T07:56:12Z)
• Efficient Physics Simulation for 3D Scenes via MLLM-Guided Gaussian Splatting [32.846428862045634]
  We present Sim Anything, a physics-based approach that endows static 3D objects with interactive dynamics.<n>Inspired by human visual reasoning, we propose MLLM-based Physical Property Perception.<n>We also simulate objects in an open-world scene with particles sampled via the Physical-Geometric Adaptive Sampling.
  arXiv  Detail & Related papers  (2024-11-19T12:52:21Z)
• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• Deep Imitation Learning for Bimanual Robotic Manipulation [70.56142804957187]
  We present a deep imitation learning framework for robotic bimanual manipulation. A core challenge is to generalize the manipulation skills to objects in different locations. We propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
  arXiv  Detail & Related papers  (2020-10-11T01:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.