Robotics
ROBOCASA365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
This paper introduces RoboCasa365, a comprehensive simulation benchmark for household mobile manipulation. It features 365 everyday tasks across 2,500 diverse kitchen environments, over 600 hours of human demonstration data, and 1600 hours of synthetically generated data. Designed for systematic evaluation of generalist policies, it supports multi-task learning, robot foundation model training, and lifelong learning. Experiments show pretraining data significantly improves downstream learning and highlights challenges in lifelong learning and transferring simulation to reality.
Executive Impact: Key Findings at a Glance
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Everyday Tasks
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Kitchen Scenes
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Total Data Hours
Deep Analysis & Enterprise Applications
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Generalist Robots
Simulation Frameworks
Training Paradigms
Recent advancements in robot learning have accelerated progress toward generalist robots, capable of performing everyday tasks in human environments. These robots require vast amounts of diverse experience data for robust policy training. RoboCasa365 addresses this by providing an unprecedented scale and diversity of simulation data.
Simulation offers a practical avenue for generating large-scale interaction datasets, enabling rapid experimentation and reproducible benchmarking. However, existing frameworks often fall short in task and environment diversity. RoboCasa365 overcomes these limitations with 365 tasks and 2,500 unique kitchen scenes.
The benchmark supports various learning settings: multi-task learning, foundation model training, and lifelong learning. Experiments demonstrate that pretraining data significantly improves downstream learning efficiency, especially for novel tasks. Lifelong learning remains a key challenge, often leading to catastrophic forgetting.
1615+
Synthetic Data Hours Generated via MimicGen
Foundation Model Training Process
Pretrain on 300 Human & 60 Atomic Tasks (2000+ hrs)
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Fine-tune on 50 Target Tasks (10%, 30%, 100% data)
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Evaluate on Atomic, Composite-Seen, Composite-Unseen
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Observe 3x Data Efficiency Gain
| Pretraining Data Strategy | Atomic Tasks (%) | Composite-Seen (%) | Composite-Unseen (%) | Average (%) |
|---|---|---|---|---|
| No Pretraining | 38.7 | 11.0 | 11.2 | 21.0 |
| Human50 (50 tasks) | 52.0 | 26.2 | 23.8 | 34.7 |
| Human300 (300 tasks) | 57.0 | 28.7 | 32.3 | 40.0 |
| Human300+MG60 (300 tasks + 60 atomic synthetic) | 56.9 | 25.4 | 22.7 | 35.9 |
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Conclusion: Training on just human data (Human300) yields better downstream results than using a mix including synthetic data, suggesting quality over sheer quantity. Increasing task diversity in pretraining (Human50 vs Human300) significantly improves performance, especially for Composite-Unseen tasks. |
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Real-World Deployment: Sim-to-Real Transfer
RoboCasa365's utility extends to real-world applications. Experiments were conducted using a DROID Panda arm in a real kitchen setting to evaluate the transferability of policies trained in simulation.
- Tasks: CloseElectricKettleLid, PickPlaceToasterOvenToCounter, PickPlaceCounterToCabinet, PlaceOnDishRack (longer horizon).
- Methodology: Compared 'Real Only' training (140 real demos) vs. 'Sim-and-Real' (mid-trained on simulation data, co-fine-tuned on real demos).
- Results: Sim-and-Real training achieved 79.8% average success rate, significantly outperforming Real Only (61.8%). This represents an 18.1% improvement.
- Key Takeaway: Simulation data from RoboCasa365 provides substantial benefits for real-world robotic policy learning, highlighting its value as a benchmark for algorithm evaluation and real-world policy learning.
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