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

Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, Yuke Zhu

arXiv:2406.02523v1cs.ROcs.AIcs.LG

TL;DR

Robotics lacks the massive, diverse datasets that have enabled scaling in other AI domains. RoboCasa uses realistic, generative-AI-enhanced simulation, systematic kitchen tasks, human demonstrations, and automated trajectories to enlarge robot-learning datasets; experiments show synthetic data supports scaling and real-world task learning. The framework remains limited by low composite-task fine-tuning performance and undesirable behaviors in some generated trajectories.

  • Problem

    Robotics lacks massive robot datasets capturing the diversity and complexity of the real world, limiting progress toward reliably deployable general-purpose robots.

  • Method

    RoboCasa combines realistic diverse kitchen simulation, generative AI assets and tasks, 100 evaluation tasks, human demonstrations, and automated trajectory generation for large-scale imitation learning.

  • Results

    Synthetic simulation data shows a clear scaling trend for robot policy learning and increases task success when co-trained for real-robot deployment.

  • Takeaways & Limitations

    Large-scale synthetic trajectories can enlarge multi-task robot-learning datasets and support transfer to real-world tasks within the demonstrated scope.

  • Takeaways & Limitations

    Fine-tuning on composite tasks remains relatively low-performing, and some generated trajectories contain jerky motions and collisions.

Abstract

from arXiv · show

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. Videos and open-source code are available at https://robocasa.ai/

I. INTRODUCTION

RoboCasa addresses the scarcity of diverse robotic training data by using realistic simulation to scale kitchen environments, tasks, and datasets. It combines generative scene construction, systematic task design, human demonstrations, and automated trajectory generation, with experiments showing benefits for policy learning and real-world transfer.

  • Motivation: Robotic data remains scarce and insufficiently diverse for reliably deploying general-purpose robots, motivating scalable alternatives to real-world collection.Collecting ever-larger real-world datasets would require unrealistic capital and labor.
  • RoboCasa Framework: RoboCasa provides realistic, diverse kitchen simulation with multiple layouts, interactable furniture and appliances, over 2,500 objects across more than 150 categories, and cross-embodiment robot support.Generative AI tools contribute object assets and environment textures.
  • Datasets: RoboCasa combines high-quality human demonstrations across all tasks with 100K additional MimicGen trajectories for atomic tasks.The resulting multi-task dataset includes large-scale synthetically generated trajectories.
  • Results: Generated data significantly improves generalization, while co-training with simulation data increases task success in real-robot deployment.These findings support simulation as a path for scaling robot policy learning.
  • Tasks: The framework includes 100 evaluation tasks: 25 atomic tasks for foundational sensorimotor skills and 75 composite tasks generated with large language model guidance.Composite tasks model naturalistic kitchen activities such as washing dishes, frying, and restocking cabinets.

II. RELATED WORK

RoboCasa is positioned among robotics simulators and dataset efforts by combining room-scale mobile manipulation, realistic physics, photorealistic rendering, multiple embodiments, broad task and object collections, and large demonstration datasets.

  • Simulation Frameworks: RoboCasa combines room-scale scenes, mobile manipulation, realistic physics, photorealistic rendering, multiple embodiments, and large collections of tasks and objects.Other frameworks often support only subsets of these capabilities or smaller portions of rooms.
  • Simulation Frameworks: Generative AI contributes potentially limitless diversity in RoboCasa's scenes and tasks alongside its broad simulation capabilities.The framework incorporates AI-generated tasks and scene-related content while retaining large-scale environments and objects.
  • Datasets and Benchmarks: RoboCasa provides large-scale task demonstrations through human teleoperation and the MimicGen system, unlike many other simulation platforms.The paper also analyzes imitation-learning agents across its large task collection.
  • Datasets and Benchmarks: Robot datasets can be collected through trial-and-error, human teleoperation, or algorithmic simulation-based trajectory generation, each involving distinct scaling constraints.Algorithmic generators may rely on privileged information and hand-designed heuristics, while some LLM pipelines require careful engineering.
  • Learning from Large Offline Datasets: RoboCasa trains offline policies with behavioral cloning on large demonstration datasets and also considers diffusion models.Behavioral cloning imitates actions in the dataset, whereas offline reinforcement learning uses rewards to prefer certain actions.

A. Core Simulation Platform

RoboCasa extends RoboSuite into a scalable, room-scale kitchen simulator with diverse layouts, interactive appliances, and a large library of kitchen objects.

  • Core platform: RoboCasa extends RoboSuite with support for room-scale environments and mobile manipulators while retaining physical realism, speed, and modularity.The framework inherits RoboSuite environment formats and robot controllers and adds mobile-manipulator support.
  • Scene diversity: 10 kitchen floor plans span basic apartment layouts to elaborate high-end designs, with configurable architectural styles.The layouts are compiled from home design and architecture magazines.
  • Interactive appliances: Dozens of appliances support articulated interactions such as opening microwave doors, twisting stove knobs, and triggering burner state changes.Appliance interactions include both geometric articulation and state changes that simulate effects such as heat.
  • Task scope: RoboCasa organizes kitchen activities into a comprehensive suite of 100 tasks for simulation and multi-task dataset development.The task suite accompanies the simulator and supports a wide array of possible kitchen activities.

A. Atomic Tasks: Building Blocks of Behavior

RoboCasa structures household behavior around foundational atomic skills and sequences them into semantically meaningful composite activities, while using human demonstrations and automated generation to scale data collection.

  • Atomic tasks: 25 atomic tasks cover eight foundational skills: pick-and-place, doors, drawers, knobs, levers, buttons, insertion, and navigation.These tasks are intended as building blocks for more complex long-horizon behaviors, while deformable manipulation remains outside the current skill list.
  • Composite tasks: Composite tasks sequence skills into semantically meaningful activities such as cooking and cleaning, with LLM guidance used to capture diverse household behaviors.LLMs suggest high-level activities and representative tasks, and can define many unique tasks at scale.
  • Demonstration collection: Human teleoperation provides 1,250 demonstrations: 50 demonstrations for each of the 25 atomic tasks across randomized kitchen scenes.Scenes vary in floor plan, kitchen style, and AI-generated textures.
  • Automated generation: MimicGen synthesizes demonstrations by decomposing seed trajectories into object-centric manipulation segments and adapting them to novel scenes.RoboCasa specifies subtask sequences for atomic skills and reuses automated subtask-end metrics across demonstrations.
  • Data-generation caveat: MimicGen generation attempts are not always successful, so RoboCasa uses rejection sampling to retain successful trajectories and parallelizes generation across simulation processes.The procedure is designed to accelerate data generation while retaining task-successful samples.

V. EXPERIMENTS

The experiments evaluate imitation learning on atomic tasks, comparing human demonstrations with machine-generated data at multiple dataset scales. Larger generated datasets improve success and can surpass human-data training, while task difficulty varies by skill diversity and dexterity.

  • The study compares four multi-task datasets: Human-50, Generated-3000, Generated-300, and Generated-100 across 24 atomic tasks.Generated-3000 contains 72,000 demonstrations across 24 tasks; Generated-300 and Generated-100 contain 7,200 and 2,400 trajectories, respectively.
  • 47.6% success rate with the fully generated dataset compared with 28.8% using human data, demonstrating a substantial improvement on atomic tasks.
  • Evaluation uses 50 trials across five fixed scenes, including unseen object instances and two unseen scene styles, to test generalization.Policies are trained with behavioral cloning using the BC-Transformer implementation, with AI-generated training textures and human-curated evaluation textures.
  • Increasing machine-generated data from 100 to 300 to 3,000 demonstrations per task steadily improves overall policy performance.The compared datasets contain 100, 300, or 3,000 generated demonstrations per task, alongside 50 human demonstrations per task.
  • Tasks with greater object-category diversity or higher dexterity are harder to learn than simpler door- and-drawer opening tasks.Pick-and-place tasks involve dozens of object categories, whereas door opening and closing involves six door instances; insertion is also challenging because of dexterity.

B. Imitation Learning for Composite Tasks

Composite tasks require multiple skills and longer horizons, making them substantially harder than atomic tasks. Fine-tuning from atomic-task pretraining outperforms learning from scratch but still produces limited robustness.

  • Composite tasks are evaluated with separate single-task policies because their multiple skills, longer horizons, and added subtleties increase difficulty.Each task uses 50 human demonstrations for comparison between Scratch and Fine-tuning.
  • Fine-tuning uses a policy pretrained on the full MimicGen atomic-task dataset and then adapts it with 50 demonstrations for each composite task.
  • Fine-tuning achieves non-zero success on 4/5 composite tasks, whereas the Scratch baseline achieves none on 4/5 tasks.
  • Common failures involve fine-grained manipulation and transitions between task stages, although fine-tuned models show more robust picking and placing.The authors attribute the qualitative improvement to the large pretraining dataset of atomic behaviors.
  • The benchmark remains difficult, leaving policy architecture, learning algorithms, and fine-tuning strategy as important areas for improvement.

C. Transfer to Real World Environments

The real-world study tests whether simulated trajectories can complement limited real demonstrations for kitchen manipulation. Co-training real and simulation data improves success over real-only training, including on unseen objects.

  • The evaluation covers three real-kitchen pick-and-place tasks involving transfers between the counter, sink, and cabinet.Each task has 50 demonstrations across five distinct object categories.
  • Real + Sim co-training combines target-task real demonstrations with MimicGen demonstrations from all simulated single-stage tasks.The Real-only baseline uses only real demonstrations for the target task.
  • 24.4% average success rate with Real + Sim versus 13.6% with Real only on seen objects, a relative improvement of 79%.Results are averaged over three seeds and evaluated on five seen and three unseen object categories.
  • Simulation data still improves performance on unseen objects, although performance is lower than on seen objects.The authors associate this benefit with the simulator’s visual and physical realism and rich diversity.

VI. CONCLUSION

RoboCasa is a large-scale kitchen-focused simulation framework combining diverse scenes, assets, tasks, generative AI, and robot trajectories. Experiments support synthetic-data scaling, while composite-task performance, trajectory quality, human guidance, and kitchen scope remain limitations.

  • RoboCasa provides 120 realistic scenes, 2,500+ 3D objects across 150+ categories, 100 tasks, and 100K+ trajectories.
  • Generative AI supplies text-to-3D object assets, text-to-image environment textures, and LLM-guided kitchen activities and tasks.
  • Synthetic simulation data is useful for scaling robot policy learning.
  • Fine-tuning on composite tasks remains relatively low-performing, and generated trajectories can contain jerky motions and collisions.The authors propose stronger policies, improved learning algorithms, and automatic trajectory filtering as future directions.
  • Task implementation still requires human guidance, and the current release is limited to kitchen environments and tasks.

A. Atomic Tasks

RoboCasa defines 25 atomic tasks spanning eight foundational robot skills, with language-disambiguated variants and filtering of flawed composite-task suggestions.

  • The 25 atomic tasks cover pick-and-place, doors, drawers, knobs, levers, buttons, insertion, and navigation.
  • Task variants use language to disambiguate goals, objects in clutter, appliance targets, burners, and navigation destinations.
  • Composite-task suggestions are generated from everyday kitchen activities identified with ChatGPT and manually selected from candidate responses.
  • The authors filter suggestions with logical flaws, including nonexistent objects, improper skill use, and attempts to grasp unsuitable utensils.

C. Datasets

The atomic-task datasets use human and machine-generated data rendered with randomly sampled AI-generated textures, while public releases will support Omniverse rendering.

  • The experiments combine human datasets with machine-generated datasets across 25 atomic tasks.
  • Images are rendered with randomly sampled AI-generated textures replacing each scene’s native textures.
  • The datasets use the lightweight MuJoCo renderer because of their high volume and time constraints.
  • The public release will let users render all datasets with the Omniverse renderer.

IX. POLICY LEARNING IMPLEMENTATION

Policy learning uses a language-conditioned BC-Transformer and compares it with diffusion policy, while the section also catalogs representative tasks and task families.

  • Policy architecture: The BC-Transformer uses 10 past observations and a language goal to output the next 10 robot actions, replanning after the first action.
  • Policy architecture: The policy encodes language goals with a CLIP sentence encoder and combines proprioceptive information with images from three cameras.
  • Training: The model has approximately 20M trainable parameters and is trained for 500k gradient steps at a learning rate of 1e-4 with warmup.
  • Policy comparison: BC-Transformer achieves 56% success on PickPlaceCounterToSink, compared with 12% for Diffusion Policy.
  • Task examples: Representative task listings include pick-and-place transfers between counters and cabinets, coffee preparation, and drying dishes.
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