RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots (2406.02523v1)

Abstract: Recent advancements in 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 LLMs. 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. Videos and open-source code are available at https://robocasa.ai/

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

The authors of the paper titled "RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots" present a formidable advance in robotic simulation frameworks, specifically designed to amplify the capabilities of generalist robots through extensive synthetic data generation. This research acknowledges the pivotal role that scaled data plays in the development of AI but highlights that robotic data, unlike other domains like NLP and computer vision, is not readily abundant. In response, this work introduces RoboCasa, a large-scale simulation environment tailored towards generating voluminous and diverse datasets that facilitate comprehensive robotic learning in domestic settings, specifically focusing on tasks ubiquitous within kitchen environments.

Core Contributions and Methodologies

RoboCasa is structurally underpinned by four key pillars:

1. Diversity in Assets: The simulation framework boasts substantial realism and variability, featuring over 120 different kitchen scenes and in excess of 2,500 unique 3D objects. This asset library is further enriched through generative AI tools, which convert text descriptions into three-dimensional models and also into environmental textures.
2. Cross-Embodiment Capabilities: The framework supports diverse robot models, including both mobile manipulators and humanoids, thus broadening the scope of tasks that can be simulated and learned.
3. Task Diversity: Utilizing LLMs, the paper introduces innovative methodologies to create and systematically evaluate 100 distinct tasks, encompassing both atomic tasks and composite activities that require the execution of numerous skills in sequence.
4. Massive Training Datasets: Integrating high-quality human demonstrations with synthetically generated data (amounting to over 100,000 trajectories), RoboCasa drastically mitigates the human labor required in data collection while ensuring data volume remains scalable.

The authors provide strong experimental evidence that further validates the framework. Specifically, the use of generated data yields a marked improvement in the performance of imitation learning policies, and a clear scalable trend emerges — as data volume increases, so does the generalization and performance capability of the trained robot.

Experimental Insights and Implications

The richness of RoboCasa's simulations is demonstrated through various empirical evaluations. Traditional human-collected data is significantly outperformed by models trained on synthetic datasets produced by MimicGen, an automated data generation system. This points towards an economical yet effective approach to generating large datasets necessary for enhancing the learning process.

While atomic tasks showed promising improvements, composite tasks, which require integrating multiple skills, exhibited challenges in achieving high success rates. The paper suggests potential improvements by leveraging adaptation strategies and more nuanced policy training methodologies. Notably, knowledge transfer to real-world robotics settings was also explored, where policies co-trained with synthetic data substantially outperformed those trained solely on real-world data, indicating the robustness and transferability potential of simulation-augmented learning.

Future Directions

The paper opens several pathways for future research:

• Enhanced Policy Learning: Exploring advanced architectures and adaptive training algorithms that can leverage the robust dataset ecosystem provided by RoboCasa.
• Expansion Across Domains: Extending the simulation framework beyond kitchen environments to encapsulate a broader range of household and possibly industrial scenarios.
• Leveraging Advanced Generative Models: Full automation of creating new scenes, tasks, and even code implementations through emerging capabilities of LLMs could further enhance RoboCasa's applicability and the diversity of scenarios it encompasses.

In conclusion, RoboCasa represents a significant enhancement in facilitating robot training through realism-rich, diverse simulations, reinforcing the belief that simulation-centric approaches can play a pivotal role in the evolution of generalist robots. The framework's open accessibility also democratizes research, providing the groundwork for substantially complex robotic learning within domestic environments. The implications are profound, suggesting that with continued technological advances, simulation frameworks such as RoboCasa could bridge the gap between robotic capabilities in constrained environments and their unfettered deployment in real-world situations.