Create README.md

README.md

@@ -0,0 +1,52 @@
+---
+library_name: opentau
+tags:
+- robotics
+- vla
+- pi05
+- libero
+- manipulation
+- flow-matching
+- pytorch
+base_model: williamyue/pi05_base
+license: apache-2.0
+datasets:
+- physical-intelligence/libero
+repo_url: https://github.com/TensorAuto/OpenTau
+---
+
+# pi05_libero_continuous_state
+
+A **pi0.5 (π₀.₅)** Vision-Language-Action (VLA) model with continuous states, finetuned on the **LIBERO** robotic manipulation benchmark using the **OpenTau** training framework. This model is designed to follow natural language instructions to perform manipulation tasks in a simulated tabletop environment. The discrete states in PI05 have been swapped out for a continuous state token.
+
+**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 (with continuous state)
+- **Backbone:** PaliGemma-3B (VLM) + Gemma-300M (Action Expert)
+- **Training Data:** LIBERO (Lifelong Robot Learning) 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. The discrete states are swapped out for a continuous state token.
+
+---
+
+## Training and Evaluation
+
+### Dataset
+This model was finetuned on the **LIBERO** benchmark dataset. The LIBERO suite consists of human-teleoperated demonstrations for tabletop manipulation, covering:
+- **Spatial Generalization** (libero_spatial)
+- **Object Generalization** (libero_object)
+- **Goal Generalization** (libero_goal)
+- **Long-Horizon Tasks** (libero_10)
+
+### Results
+For detailed usage instructions, success rates, baseline comparisons, and evaluation protocols, please refer to the [OpenTau GitHub Repository](https://github.com/TensorAuto/OpenTau).