RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

The paper presents RoboCasa, a large-scale simulation framework that enables the training of generalist robots in kitchen environments, using diverse assets, cross-embodiment support, and a dataset of over 100,000 trajectories to address the challenge of data scarcity in robotics and to improve the scalability of robot learning through realistic simulations.

Abstract

Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning methods. We present RoboCasa, a large-scale simulation framework for training generalist robots in everyday environments. RoboCasa features realistic and diverse scenes focusing on kitchen environments. We provide thousands of 3D assets across over 150 object categories and dozens of interactable furniture and appliances. We enrich the realism and diversity of our simulation with generative AI tools, such as object assets from text-to-3D models and environment textures from text-to-image models. We design a set of 100 tasks for systematic evaluation, including composite tasks generated by the guidance of large language models. To facilitate learning, we provide high-quality human demonstrations and integrate automated trajectory generation methods to substantially enlarge our datasets with minimal human burden. Our experiments show a clear scaling trend in using synthetically generated robot data for large-scale imitation learning and show great promise in harnessing simulation data in real-world tasks.

Method

The research methods used in the RoboCasa paper involved the development of a large-scale simulation framework to address the challenge of data scarcity in robotics. This included creating diverse assets such as kitchen scenes and 3D objects, leveraging generative AI tools for asset creation, designing tasks with the aid of large language models, compiling a large multi-task dataset through human teleoperation and automated trajectory generation, applying imitation learning techniques to train policies, conducting systematic experiments for performance evaluation, and ensuring cross-embodiment support for various robotic forms within the simulation environment.

Main Finding

The main finding of the RoboCasa paper is that large-scale simulation frameworks, like RoboCasa, can effectively utilize synthetic data and generative AI tools to train generalist robots on a diverse range of tasks, demonstrating a clear scaling trend in performance with dataset size and showing promise for the transfer of learned skills to real-world applications.

Conclusion

The conclusion of the RoboCasa paper is that the authors have presented a large-scale simulation framework that effectively addresses the challenge of data scarcity in robotics by providing a rich and diverse training environment for generalist robots. Through the use of generative AI tools for asset creation, large language models for task generation, and a combination of human and machine-generated data for training, the framework has shown a clear scaling trend in the performance of imitation learning policies. The experiments conducted within the simulation and in real-world settings demonstrate the potential of simulation-based training to improve the generalization and task execution capabilities of robots, suggesting a promising path forward for scaling robot learning and deploying generalist robots in real-world applications.

Keywords

Robotics, Simulation, Generalist Robots, Imitation Learning, Large-Scale Datasets, Generative AI, Diverse Tasks, Cross-Embodiment Support, Kitchen Environments, Language Models, 3D Objects, Realism, Dataset Generation, Task Generalization, RoboCasa, RoboSuite, MimicGen, Behavioral Cloning, Transformer-based Policies, Real-World Transfer, Multi-Task Learning, Composite Tasks, Atomic Tasks, Human Demonstrations, Synthetic Data, Photorealistic Rendering, Robot Learning, Embodied AI, Kitchen Activities, Robot Skills, Robot Learning Scalability, Robot Task Performance, Robot Simulation Frameworks, Robot Datasets, Robot Benchmarks, Robot Learning Algorithms, Robot Policy Architectures, Robot Task Generalization, Robot Task Transfer, Robot Task Evaluation, Robot Task Success, Robot Task Diversity, Robot Task Complexity, Robot Task Realism, Robot Task Simulation, Robot Task Execution, Robot Task Performance Evaluation, Robot Task Performance Metrics, Robot Task Success Rates, Robot Task Generalization Metrics, Robot Task Transfer Metrics, Robot Task Evaluation Metrics, Robot Task Performance Improvement, Robot Task Learning Efficiency, Robot Task Learning Effectiveness, Robot Task Learning Scalability, Robot Task Learning Methodologies, Robot Task Learning Frameworks, Robot Task Learning Datasets, Robot Task Learning Benchmarks, Robot Task Learning Algorithms, Robot Task Learning Policies, Robot Task Learning Architectures, Robot Task Learning Evaluation, Robot