RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning (2504.18904v1)

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.

• The paper introduces RoboVerse, a unified framework combining a simulation platform (MetaSim), synthetic dataset (10M+ transitions), and benchmarks for scalable and generalizable robot learning.
• Key to RoboVerse is the MetaSim infrastructure, enabling cross-simulator integration, hybrid simulation, and cross-embodiment transfer to maximize data utilization and benchmarking.
• Experimental validation shows RoboVerse significantly improves various learning paradigms like imitation and reinforcement learning, boosting policy learning and sim-to-real transfer performance.

RoboVerse: Framework for Scalable and Generalizable Robot Learning

The paper "RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning" discusses the development of a comprehensive infrastructure to address the inherent challenges in robotics related to data scaling and evaluation standardization. The paper introduces RoboVerse, a multifaceted framework consisting of a simulation platform, synthetic dataset, and unified benchmarks, with the objective of advancing robot learning in scalable and generalizable ways.

Key Components

At the core of RoboVerse lies the MetaSim infrastructure, which abstracts various simulation environments into a universal interface, facilitating seamless integration across diverse simulators. MetaSim provides three critical capabilities:

1. Cross-Simulator Integration: It facilitates the transfer of tasks and data among different simulators, thus enhancing benchmarking and enabling sim-to-sim transfer for reinforcement learning.
2. Hybrid Simulation: This capability allows leveraging different simulators' strengths, such as combining precise physics engines with advanced renderers, leading to high-quality synthetic data generation.
3. Cross-Embodiment Transfer: It enables trajectory reuse across various robotic embodiments, maximizing data utilization.

Dataset and Benchmarks

RoboVerse's dataset encompasses more than 1,000 tasks and over 10 million transitions, achieved through strategies like large-scale data migration and extensive augmentation. The dataset serves multiple benchmarks, supporting imitation learning and reinforcement learning, and is constructed from comprehensive public sources and generated data.

The unified benchmarks provide a structured environment for consistent evaluation, capturing different levels of generalization through domain randomization techniques. In this way, researchers can assess the robustness of model generalization to novel scenarios or environmental conditions.

Experimental Validation

The paper presents thorough experimental validation showing that RoboVerse significantly boosts various learning paradigms such as imitation learning, reinforcement learning, and world model learning. Experiments demonstrate improved policy learning and sim-to-real transfer performance, confirming the dataset's reliability and benchmarks' robustness.

Practical and Theoretical Implications

Practically, RoboVerse can streamline the development and deployment of scalable and generalizable robotic policies across diverse tasks and settings, addressing the high resource demands associated with real-world data collection. Theoretically, it offers a foundation for advancing simulation-assisted learning algorithms, providing both breadth and depth in datasets critical for developing robust AI models.

Future Directions

The initiative opens avenues for enhancing interoperability between simulators and offers a proving ground for multi-agent and multi-task learning frameworks. Future work may focus on extending the framework's applicability to non-rigid object manipulation, optimizing pre-training strategies on large synthetic datasets, and refining sim-to-real transfer methodologies.

In summary, RoboVerse addresses key roadblocks in robotics by establishing a unified, scalable, and generalizable platform for robot learning, facilitating improvements in both data utilization and evaluation standardization. This framework promises substantial contributions to the field, underscoring the potential of synthetic data and simulation in overcoming current limitations in real-world robotic learning scenarios.