openpi

Open-source implementation of "RL Token: Bootstrapping Online RL with Vision-Language-Action Models"

ACOT-VLA-WM is an advanced Vision-Language-Action robotics framework that uses a predictive world model to generate visual subgoals, improving long-horizon robotic manipulation accuracy and robustness.

Real-time inference engine for openpi

Unofficial OpenPI extension experiment to build a more complete OpenPI-style VLA engineering stack: pi0.5 semantics, RTC, pi0.6 RECAP/MEM, and pi0.7-inspired world-model ideas.

Deployment-focused fork of OpenPI for LimX TRON2 manipulation — pi0.5 policy serving, task fine-tuning, TRON2 transforms, and real-robot client examples.

π₀ / π₀-FAST / π₀.₅ vision-language-action models adapted to Xense platforms — fine-tuning and real-world deployment on BiARX5, BiFlexiv and XTac-UMI dual-arm rigs.

Try OpenPI closed loop simulation / scenes generation / 3DGS model scenes in genie_sim

ROS2-native runtime, benchmark, and adapter hub for Vision-Language-Action models.

An end-to-end tour of embodied AI world models: Closed-loop VLA simulation, Real2Edit2Real spatial scene editing, generalization benchmarking, and in-world-model RL self-evolution using GE-Sim 2.0 & OpenPI π₀.₅.

ArcheBase Physical AI 开放电子书：VLA、世界模型、机器人学习、控制、评测与开放数据实践

End-to-end open-source stack for real Franka robots: realtime control, GELLO/VR data collection, policy training, and guarded deployment.

Reproducible AMD ROCm evaluation and full three-camera recording for BiGym, OpenPI JAX, MuJoCo and 3DGS.

Per-Group Error, Not Total MSE. Fine-tuning VLAs on the Toyota HSR. ICRA 2026 Workshop From Data to Decisions, 1st place out of 36.

Kuavo Scene2 VLA: rosbag-to-LeRobot data pipeline, OpenPI π0.5 LoRA training, and ROS/MuJoCo closed-loop deployment

基于 pi0/OpenPI 的 RM 双臂机器人 MuJoCo 仿真与策略接入项目（由 2025 年 6 月小项目整理，可能存在 bug）。

Physical Intelligence — independent third-party profile of a public API surface, by API Evangelist. Physical Intelligence (often styled "Pi" or "π") is a San Francisco-based research company building general-purpose foundation models for robotics with the stated goal of producing learning algorithms that can control any robot to do any task.

Fine-tuning π0-FAST on an RTX 4070 using synchronized camera and joint demonstrations from a custom 4-DOF teleoperation arm, with LeRobot conversion, policy serving, open/closed-loop evaluation, and a real-time dashboard.

Model-agnostic robot policy deployment with safety checks, dry-run, audit/replay, metrics and FastAPI.

Sanitized engineering notes on AI tools, infrastructure, and robotics.

Enpei Episode1 single-arm LeRobot data collection and OpenPI pi0.5 inference client