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
tPi0.5-libero
A pi0.5 (π₀.₅) Vision-Language-Action (VLA) model, 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.
For full documentation, evaluation results, and inference code, please visit the repository:
👉 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: 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.
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.