Robocasa_navigatekitchen

A pi0.5 (π₀.₅) Vision-Language-Action (VLA) model, finetuned on the ROBOCASA robotic manipulation/navigation benchmark using the OpenTau training framework. This model is designed to follow natural language instructions to perform navigation tasks in a simulated kitchen environment.

For full documentation, evaluation results, and inference code, please visit the repository:
👉 https://github.com/TensorAuto/OpenTau

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Model Details

Description

• Model Type: Vision-Language-Action (VLA) Model
• Base Architecture: π₀.₅ (pi0.5) by Physical Intelligence
• Backbone: PaliGemma-3B (VLM) + Gemma-300M (Action Expert)
• Training Data: Robocasa Benchmark
• Framework: OpenTau

Architecture

The pi0.5 architecture uses a flow-matching-based policy designed for open-world generalization. It combines a Visual Language Model (VLM) for high-level semantic understanding with a smaller "action expert" model that generates continuous joint trajectories (10-step action chunks) via flow matching.

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Training and Evaluation

Dataset

This model was finetuned on the Robocasa benchmark dataset. The Robocasa suite consists of human-teleoperated and mimicgen demonstrations for manipulation and navigation, covering:

• CloseToasterOvenDoor (Atomic)
• CloseDishwasher (Atomic)
• CloseOven (Atomic)

Results

Training on 100 Human demonstrations, our model achieves 70% , 90% and 90% success rate on CloseToasterOvenDoor, Close Dishwasher and Close Oven tasks respectively. For detailed usage instructions, success rates, baseline comparisons, and evaluation protocols, please refer to the OpenTau GitHub Repository.