title: RoboVerse: Towards a Unified Platform, Dataset and Benchmark for
    Scalable and Generalizable Robot Learning
summary: 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.
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paper_title: RoboVerse: Towards a Unified Platform, Dataset and Benchmark for
    Scalable and Generalizable Robot Learning
paper_summary: 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.
paper_ai_summary: RoboVerse provides a comprehensive framework with high-quality synthetic data, simulation platform, and unified benchmarks to address data scaling and evaluation challenges in robotics.
paper_ai_keywords: synthetic data simulation platform benchmark standardization imitation learning reinforcement learning world model learning sim-to-real transfer MetaSim simulator-agnostic physics engine