RoboBrain
| Property | Value |
|---|---|
| Author | BAAI |
| Publication | CVPR 2025 |
| Paper | arXiv:2502.21257 |
| Model Type | Multimodal LLM for Robotics |
What is RoboBrain?
RoboBrain is a groundbreaking unified brain model designed specifically for robotic manipulation tasks. It addresses key limitations in current Multimodal Large Language Models (MLLMs) by incorporating three essential capabilities: planning, affordance perception, and trajectory prediction. Built on the ShareRobot dataset, it employs a multi-stage training strategy to bridge the gap between abstract understanding and concrete robotic actions.
Implementation Details
The model utilizes a sophisticated multi-stage training approach, incorporating both general multimodal data and specialized robotic training. It supports high-resolution image processing and long video sequences, with implementations available through both Hugging Face and VLLM inference engines.
- Multi-stage training pipeline from general to specialized robotic tasks
- Integration with ShareRobot dataset for comprehensive robotic understanding
- Support for both base planning and specialized LoRA adaptations
- Flexible inference options through HF and VLLM implementations
Core Capabilities
- Task Planning: Breaks down complex manipulation instructions into manageable sub-tasks
- Affordance Perception: Recognizes and interprets object interaction possibilities
- Trajectory Prediction: Anticipates and plans manipulation trajectories
- High-resolution image and long video processing
Frequently Asked Questions
Q: What makes this model unique?
RoboBrain stands out by bridging the gap between abstract understanding and concrete robotic actions through its three-pronged approach to planning, affordance perception, and trajectory prediction. It's built on a carefully curated dataset and uses a novel multi-stage training strategy.
Q: What are the recommended use cases?
The model is ideal for robotic manipulation tasks that require complex planning and execution, particularly in scenarios where robots need to understand and interact with objects in their environment. It's especially useful for long-horizon manipulation tasks that require both high-level planning and detailed execution understanding.
