--- library_name: opentau tags: - robotics - vla - pi0 - libero - Reinforcement Learning - manipulation - flow-matching - pytorch license: apache-2.0 datasets: - physical-intelligence/libero repo_url: https://github.com/TensorAuto/OpenTau --- # moka_pot_RECAP_R0 A **pi0 (π₀) RECAP** 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. Achieves **~89% success rate** measured over **320 episodes**. **For full documentation, evaluation results, and inference code, please visit the repository:**
👉 **[https://github.com/TensorAuto/OpenTau](https://github.com/TensorAuto/OpenTau)** --- ## Model Details ### Description - **Model Type:** Vision-Language-Action (VLA) Model - **Base Architecture:** π₀ (pi0) by Physical Intelligence - **Backbone:** PaliGemma-3B (VLM) + Gemma-300M (Action Expert) + RL indicator - **Training Data:** Moka Pot Task on LIBERO (Lifelong Robot Learning) Benchmark - **Framework:** OpenTau ### Architecture The **PI0 RECAP** architecture uses a flow-matching and Reinforcement Learning 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. It uses RL to learn from good and bad episodes --- ## Training and Evaluation The Advantage Indicator (It) was set to True for only 10% of datapoints. ### Dataset This model was finetuned on the **Moka Pot task in LIBERO 10** benchmark dataset and autonomous rollouts. It consists of around 29 expert teleoperated episodes and 212 autonomous rollouts under moka_pot_libero_sft policy. ### Results For detailed usage instructions, success rates, baseline comparisons, and evaluation protocols, please refer to the [OpenTau GitHub Repository](https://github.com/TensorAuto/OpenTau). Achieves **~89% success rate** measured over **320 episodes**.