Update README.md

README.md

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----
-license: apache-2.0
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+---
+library_name: opentau
+tags:
+- robotics
+- vla
+- pi05
+- robocasa
+- manipulation
+- flow-matching
+- pytorch
+base_model: williamyue/pi05_base
+license: apache-2.0
+datasets:
+- robocasa/CloseMicrowave
+- robocasa/CloseFridge
+- robocasa/CloseCabinet
+repo_url: https://github.com/TensorAuto/OpenTau
+---
+
+# 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 manipulation/navigation tasks in a simulated kitchen environment.
+
+**For full documentation, evaluation results, and inference code, please visit the repository:**
+<br>
+👉 **[https://github.com/TensorAuto/OpenTau](https://github.com/TensorAuto/OpenTau)**
+
+---
+
+## 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.
+
+---
+
+## 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:
+- **CloseMicrowave** (Atomic)
+- **CloseFridge** (Atomic)
+- **CloseCabinet** (Atomic)
+
+### Results
+Training on 100 Human demonstrations, our model achieves **98% , 80% and 65%** success rate on CloseMicrowave, Close Fridge and Close Cabinet tasks respectively.
+For detailed usage instructions, success rates, baseline comparisons, and evaluation protocols, please refer to the [OpenTau GitHub Repository](https://github.com/TensorAuto/OpenTau).