CYBER: A General Robotic Operation System for Embodied AI
The development of world models in robotics has long been a cornerstone of advanced research, with most approaches relying heavily on vast, platform-specific datasets. These datasets, while valuable, often limit scalability and generalization to different robotic platforms, restricting their broader applicability.
In contrast, CYBER approaches world modeling from a "first principles" perspective, drawing inspiration from how humans naturally acquire skills through experience and interaction with their environment. CYBER is the first general Robotic Operational System designed to adapt to both teleoperated manipulation and human operation data, enabling robots to learn and predict across a wide range of tasks and environments. It builds with a Physical World Model, a cross-embodied Visual-Language Action Model (VLA), a Perception Model, a Memory Model, and a Control Model to help robots learn, predict, and memory across various tasks and embodiments.
At the same time, CYBER also provide millions of human operation datasets and baseline models over HuggingFace 🤗 to enhance embodied learning, and experimental evalaution tool box to help researchers to test and evaluate their models in both simulation and real world.
🛠️ Modular Components
CYBER is built with a modular architecture, allowing for flexibility and customization. Here are the key components:
- 🌍 World Model: Learns from physical interactions to understand and predict the environment.
- 🎬 Action Model: Learns from actions and interactions to perform tasks and navigate.
- 👁️ Perception Model: Processes sensory inputs to perceive and interpret surroundings.
- 🧠 Memory Model: Utilizes past experiences to inform current decisions.
- 🎮 Control Model: Manages control inputs for movement and interaction.
📰 Release
- 2024-11-18: 🌍 World Model supports new tokenizer model Cosmos-Tokenizer and new dynamic model Deep Planning Network
- 2024-10-23: 🌍 World Model is now available. Additional models will be released soon.