Global

RoboICL is an in-context robot-control framework that adapts a frozen general-purpose vision-language model from demonstrations and its own execution history. It requires no robot-specific parameter update and no learned vision-language-action model.

OpenWAM is an open research stack for systematically developing World-Action Models (WAMs). It turns tightly coupled design choices into modular components and controlled experiments. It consists of:

A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs. 3, Kimi-K3 and more.

Zetta is an efficient closed-loop embodied harness for self-evolving physical intelligence. It evolves code-based runtime critics and recovery skills online while keeping the base policy frozen. Project Page:

The in-context human video generation pipeline starts from task-sampled robot trajectories that retain executable actions.

One CLI for all your robots. Connect them, command them, and let them work together, each with an LLM for a brain (Claude, OpenAI, Gemini, Grok, or local via Ollama or vLLM), VLAs for arms and decision LLMs (Jev, Laya, Kev).

Flex-π is a 6B-parameter world-action model for robot manipulation. It jointly predicts future RGB, 3D pointmaps, DINOv3 semantics, and actions in training, then deploys as a VLA, as a full world-action model, or as anything in between — all from a single checkpoint.

Official implementation of StreamPI, built on openpi , with additional, fully validated support for JAX multi-node distributed training beyond the standard single-node setup, enabling substantially more efficient training without compromising model performance .

A single-GPU, end-to-end research pipeline with a falsifiable core: every imagined action is replayed in the real simulator before anything is claimed about it.