Understanding the Target Audience for VeBrain
The primary audience for VeBrain includes AI researchers, robotics engineers, and tech industry leaders. These professionals are in search of innovative solutions to enhance the capabilities of robots across various sectors, including manufacturing and healthcare. Their main challenges include:
- Integrating multimodal understanding with physical robot control.
- Scaling robotic solutions across diverse environments.
- Achieving precise, real-time decision-making in robotics.
Their goals often encompass:
- Developing autonomous systems that can perceive, reason, and act in real-world contexts.
- Improving the efficiency and adaptability of robots for various tasks.
- Staying ahead of advancements in AI and robotics.
Interests in the field include new AI methodologies, applications of robotics in business, and emerging technologies in multimodal AI frameworks. These professionals typically prefer technical documentation, research publications, and informative webinars for communication.
Bridging Perception and Action in Robotics
Multimodal Large Language Models (MLLMs) represent a significant leap in enabling machines like robotic arms and legged robots to understand their surroundings, interpret scenarios, and perform meaningful actions. The integration of this type of intelligence into physical systems is crucial for moving towards fully autonomous machines capable of planning and executing actions based on contextual understanding.
Limitations of Prior VLA Models
Traditionally, robot control has relied on vision-language-action (VLA) models. These models are designed to convert visual observations into control signals, but they have notable limitations:
- Performance tends to degrade during complex tasks, especially in diverse or long-horizon operations.
- They struggle to generalize across different environments or types of robots.
Introducing VeBrain: A Unified Multimodal Framework
VeBrain, developed by researchers from Shanghai AI Laboratory, Tsinghua University, and SenseTime Research, offers a forward-thinking framework that treats robot control as text-based tasks within a 2D visual space. This approach aligns with how MLLMs operate, fostering a seamless integration of multimodal understanding, spatial reasoning, and robotic control.
VeBrain is supported by the VeBrain-600k dataset, which includes over 600,000 multimodal task samples, encompassing robot motion and reasoning steps.
Technical Components: Architecture and Robotic Adapter
The architecture of VeBrain is built on Qwen2.5-VL and features a specialized robotic adapter comprised of four key modules:
- The point tracker updates 2D keypoints as the robot’s perspective changes.
- The movement controller translates 2D keypoints into 3D movements by merging image data with depth maps.
- The skill executor maps predicted actions to pre-trained robotic skills.
- The dynamic takeover module monitors failures to maintain control when necessary.
This closed-loop system empowers robots to make informed decisions, take action, and self-correct in various environments.
Performance Evaluation Across Multimodal and Robotic Benchmarks
VeBrain was rigorously evaluated across 13 multimodal and 5 spatial benchmarks, showcasing impressive results:
- 5.6% improvement on the MMVet benchmark compared to Qwen2.5-VL.
- A score of 101.5 on the CIDEr metric for ScanQA.
- A score of 83.7 on MMBench.
- An average score of 39.9 on the VSI benchmark, outperforming Qwen2.5-VL’s score of 35.9.
- 86.4% success rate across seven-legged robot tasks, significantly surpassing VLA (32.1%) and π0 (31.4%).
- 74.3% success rate on robotic arm tasks, outperforming others by up to 80%.
Conclusion
The VeBrain framework marks a significant advancement in embodied AI, redefining robot control as a language task. This integration allows high-level reasoning and low-level actions to coexist, bridging the gap between image understanding and robot execution. With strong performance metrics, VeBrain signals a shift towards more unified, intelligent robotic systems capable of autonomous operations across diverse tasks and environments.
