Unified Robotics Description Format (URDF) in EmbodiedGen

Last updated: June 15, 2025

Significance and Background

The Unified Robotics Description Format (URDF °) is a widely adopted XML ° standard for representing the structure and physical properties of robots—including their kinematics ° (links and joints), inertia, geometric meshes, and actuation properties. URDF is supported across major simulation platforms, including OpenAI ° Gym, MuJoCo °, Isaac Lab °, and SAPIEN, enabling seamless modeling, simulation, and physical control of robots and interactive 3D assets ° (Xinjie et al., 12 Jun 2025 ° ).

EmbodiedGen is a generative toolkit that leverages URDF as the output layer of a multi-stage process, enabling the scalable, automated production of diverse and physically annotated 3D assets and scenes for embodied intelligence ° research. EmbodiedGen addresses the bottlenecks and scaling challenges associated with manual creation and annotation of 3D assets and scenes, making them accessible for downstream use in simulation and embodied agent evaluation [(Xinjie et al., 12 Jun 2025 ° ), Section 1].

URDF as a Foundation for 3D Asset Generation

URDF models, as created in EmbodiedGen, comprise:

• Links: Rigid bodies defined by meshes (OBJ, STL, etc.), mass, and inertia.
• Joints: Articulations (revolute, prismatic, etc.) with defined axes, limits, and transforms relating links.
• Visual and Collision Geometry: Assignment of appearance and collision boundaries to support both rendering and physical simulation °.
• Physical Properties: Including mass, friction, and real-world scale for accurate simulation behavior [(Xinjie et al., 12 Jun 2025 ° ), Section 3.1].

A key technical function within EmbodiedGen is the alignment of generatively produced meshes to real-world scale and augmentation with physical parameters. Generative models often output meshes at arbitrary scales. EmbodiedGen employs a physics restoration process: a LLM agent ° (e.g., GPT-4o or Qwen) estimates real-world height from the rendered view, and the mesh is scaled to match this estimate:

$\mathbf{v}_i' = S \cdot \mathbf{v}_i, \quad S = \frac{h_{real}}{h_{mesh}}$

where $h_{real}$ is the agent-inferred height, and $h_{mesh}$ is the axis-aligned bounding box ° height of the original mesh [(Xinjie et al., 12 Jun 2025 ° ), Section 3.1].

Physical attributes such as mass and friction coefficients ° are also estimated by the same agent based on object category and visual cues and written as URDF <inertial> and material tags °, respectively.

EmbodiedGen Pipeline: URDF in Modular Generative Workflows

EmbodiedGen is structured with six modules, each responsible for stages of 3D asset and scene generation °, culminating in URDF asset creation [(Xinjie et al., 12 Jun 2025 ° ), Section 3.1–3.7]:

Image-to-3D and Text-to-3D

• Image-to-3D °: Generates meshes or 3D Gaussian ° Splatting (3DGS) assets from single images. Meshes are inspected and rescaled to real-world units, annotated with estimated mass, friction, and semantic information, then exported as URDF assets.
• Text-to-3D: Generates images from text prompts ° (using a model like Kolors), then produces 3D meshes ° (Trellis, DIPO), and annotates them in the same manner for URDF export [(Xinjie et al., 12 Jun 2025 ° ), Section 3.2; Figure 9].

URDF generation after these steps ensures that each asset is ready for physics-based simulation ° and manipulation.

Texture Generation

EmbodiedGen uses GeoLifter, a geometry-guided diffusion model, to generate high-resolution, spatially consistent UV textures ° for each mesh [(Xinjie et al., 12 Jun 2025 ° ), Section 3.4]. In the exported URDF, these textures are referenced in <visual> and <material> tags:

<visual>
  <geometry>
    <mesh filename="object.obj" scale="1 1 1"/>
  </geometry>
  <material name="tex">
    <texture filename="object_tex.png"/>
  </material>
</visual>

Articulated Object Generation

The platform predicts segmentation and kinematic structure ° for articulated assets from dual-state images or text prompts, creating URDF <joint> entries to represent real articulations:

<joint name="drawer_joint" type="prismatic">
  <parent link="cabinet_base"/>
  <child link="drawer"/>
  <origin xyz="..." rpy="..."/>
  <axis xyz="1 0 0"/>
  <limit effort="1.0" lower="0" upper="0.3" velocity="0.2"/>
</joint>

Scene and Layout Generation

Scene synthesis ° relies on panoramic imaging ° (Diffusion360, Pano2Room) and LLM °-driven layout reasoning to compose complex scenes. All objects and static scene elements are encoded as URDF links; articulated or movable sub-objects are linked via joints in the URDF [(Xinjie et al., 12 Jun 2025 ° ), Sections 3.6–3.7]. This “master URDF” can represent an entire world model, with structured kinematic or positional relations between constituent assets.

Module-to-URDF Mapping: Summary Table

Current Applications

EmbodiedGen has demonstrated the use of URDF-generated assets in:

• Digital twins ° and real-to-sim ° transfer: Real-world images are processed into meshes and URDF files ° used for benchmarking or manipulation in simulators such as Isaac Lab or MuJoCo [(Xinjie et al., 12 Jun 2025 ° ), Section 4; Figure 21].
• Object asset libraries: Prompt-driven, batch generation of diverse objects (e.g., mugs, hammers, articulated furniture) exported as mesh+URDF bundles [(Xinjie et al., 12 Jun 2025 ° ), Figures 18–19].
• Articulated assets: Complex furniture or robotic parts with accurately described joints, limits, and connectivity, supporting interactive manipulation in simulation [(Xinjie et al., 12 Jun 2025 ° ), Figure 10].
• Scene assembly: Automated composition of entire 3D environments ° using LLM-generated spatial layouts, outputted as structured URDFs for direct import [(Xinjie et al., 12 Jun 2025 ° ), Figure 22].

All pipelines, annotations, and assets are open-sourced for community evaluation and extension project page.

Limitations and Considerations

URDF supports a broad suite of physical and kinematic attributes, but reported limitations persist. Notably, URDF cannot natively describe cyclic kinematic topologies (closed loops) or non-rigid/soft bodies without significant extension, restricting its expressiveness for some classes of robots and objects [(Xinjie et al., 12 Jun 2025 ° ), Section 5]. For articulated structures requiring such topologies, ongoing developments in extended URDF standards may be required.

Automated recovery of real-world scale and physical attributes, while effective for batch generation, relies on AI-generated ° estimation and may require user validation or manual correction for use cases with high physical accuracy requirements [(Xinjie et al., 12 Jun 2025 ° ), Section 3.3].

Emerging Trends and Directions

Key trends in the deployment of URDF within generative pipelines ° such as EmbodiedGen include:

• Automation of content creation: Near-total automation of geometry, texture, physicalization, and semantic annotation ° for simulation-ready assets reduces effort and cost [(Xinjie et al., 12 Jun 2025 ° ), Section 4].
• Extension to richer semantics: While URDF facilitates assignment of physical attributes, further schema evolution ° may incorporate dynamic, material, and world-level properties suited to next-generation embodied AI applications ° [(Xinjie et al., 12 Jun 2025 ° ), Section 5].
• Scene-level integration: URDF's modularity and compositional design ° are increasingly leveraged for scene-level assembly in simulation and interactive world generation °.

Speculative Note

Future schema evolution for URDF could include standardized support for higher-order materials, deformable bodies, or dynamic semantic attributes °. As generative pipelines continue to mesh with robotics and simulation standards, deeper convergence between AI-driven asset creation and physics-based digital twin infrastructures is likely [citation needed].

All claims and technical details are sourced from EmbodiedGen (Xinjie et al., 12 Jun 2025 ° ) and its publicly available documentation. For implementation details and demonstrations, refer to the EmbodiedGen codebase and documentation.