Unified digital gene and representation systems, large foundation models, and collaborative system evolution
| Repository | Description |
|---|---|
| Digital Gene | Brief Description |
| End-to-end Causal Reasoning and Closed-loop Interactive Learning | Safe and robust robotic action with causal reasoning |
| Embodied Companion: Data and Representation | Complex manipulation skills directly from human demonstration |
| IL-Studio: One-stop Platform for Imitation Learning Data | A plug-and-Play imitation-learning playground for robotics |
| Commonsense Embodied Decision-making | Any House Any Task embodied planning model |
| OpenEAI-Platform: Open-source Embodied Artificial Intelligence Hardware-Software Unified Platform | One stop open source for embodied foundational model hardware and software |
Digital Gene
DetailsEnd-to-end Causal Reasoning and Closed-loop Interactive Learning
Embodied Companion: Data and Representation
Embodied Companion builds large-scale human–robot companion data and unified representations to transfer everyday human manipulation into robot-executable skills, empowering downstream VLA models.IL-Studio: One-stop Platform for Imitation Learning Data
IL-Studio is an open-source repository that lets researchers and engineers jump-start imitation-learning experiments on popular robot manipulation benchmarks with minimal friction. The entire training, evaluation, and deployment pipeline has been carefully modularized so that you can swap-in your own policy, environment, or dataset without touching the rest of the stack.Commonsense Embodied Decision-making
Commonsense Embodied Decision-making is an open-source research framework for embodied AI, aimed at enabling intelligent agents, such as robots, to leverage commonsense knowledge like humans to understand complex instructions and make decisions.
AHAT (Any House Any Task) is an end-to-end Large Language Model (LLM) to serve as the agent's reasoning and decision-making core.