Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models

2025-12-28

AI Summary

Conventional robot description formats (e.g., URDF, SDF) encode only basic kinematic, dynamic, and geometric data, forcing downstream tasks—such as simulation, motion planning, and control—to redundantly recompute derived information, resulting in computational inefficiency, fragmented implementations, and inconsistent standards. Method: We propose the Universal Robot Description Directory (URDD), a modular, structured, and extensible framework for representing derived robot data, enabling cross-toolchain sharing and on-demand loading. We implement a Rust-based URDF-to-URDD converter and integrate Bevy-engine visualization with a JavaScript/Three.js web viewer, exporting modular JSON/YAML artifacts. Contribution/Results: Evaluated across diverse robotic platforms, URDD significantly increases information density and directly supports core algorithms—including forward/inverse kinematics and collision detection—thereby advancing robot model standardization and enabling a paradigm shift toward reusable, interoperable robot descriptions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Description Logics
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web search models and ranking

Abstract

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work.

Problem

• Standard robot files lack rich data, causing redundant computations.
• Existing specifications limit standardization and fragment implementations.
• Need a unified, extensible resource for shared robotics frameworks.

Innovation

• URDD organizes robot data into structured JSON/YAML modules
• Open-source toolkit auto-generates URDDs from URDF files
• URDD enables unified, extensible standards across robotics frameworks