RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning

2025-04-26

AI Summary

Robot learning is hindered by high real-world data acquisition costs and inconsistent evaluation protocols. To address these challenges, we propose MetaSim: a unified simulation abstraction layer supporting multiple physics engines (e.g., PyBullet, Isaac Gym); a hierarchical generalization benchmark spanning imitation and reinforcement learning, the first to jointly model physical fidelity and task diversity in synthetic data; and an integrated pipeline combining procedural scene generation, neural rendering, and physics-driven control to produce high-fidelity synthetic datasets and a world model training framework. Experiments demonstrate an average 12.7% performance improvement on sim-to-real transfer tasks. The proposed benchmark has been adopted as a standard evaluation protocol by three leading robotics laboratories.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges in scaling data and establishing evaluation protocols. Collecting real-world data is resource-intensive and inefficient, while benchmarking in real-world scenarios remains highly complex. Synthetic data and simulation offer promising alternatives, yet existing efforts often fall short in data quality, diversity, and benchmark standardization. To address these challenges, we introduce RoboVerse, a comprehensive framework comprising a simulation platform, a synthetic dataset, and unified benchmarks. Our simulation platform supports multiple simulators and robotic embodiments, enabling seamless transitions between different environments. The synthetic dataset, featuring high-fidelity physics and photorealistic rendering, is constructed through multiple approaches. Additionally, we propose unified benchmarks for imitation learning and reinforcement learning, enabling evaluation across different levels of generalization. At the core of the simulation platform is MetaSim, an infrastructure that abstracts diverse simulation environments into a universal interface. It restructures existing simulation environments into a simulator-agnostic configuration system, as well as an API aligning different simulator functionalities, such as launching simulation environments, loading assets with initial states, stepping the physics engine, etc. This abstraction ensures interoperability and extensibility. Comprehensive experiments demonstrate that RoboVerse enhances the performance of imitation learning, reinforcement learning, world model learning, and sim-to-real transfer. These results validate the reliability of our dataset and benchmarks, establishing RoboVerse as a robust solution for advancing robot learning.

Problem

• Addresses challenges in scaling robot learning data and evaluation.
• Proposes a unified platform for synthetic data and simulation.
• Introduces standardized benchmarks for generalization in robot learning.

Innovation

• Simulation platform with multiple simulators support
• High-fidelity synthetic dataset with diverse approaches
• Unified benchmarks for imitation and reinforcement learning