VeBrain: A Unified Multimodal AI Framework for Visual Reasoning and Real-World Robotic Control

Understanding the Target Audience for VeBrain

The target audience for VeBrain includes AI researchers, robotics engineers, and business leaders in the tech industry. These individuals are often seeking innovative solutions to enhance robotic capabilities in various applications, from manufacturing to healthcare.

Their key pain points include:

• Inability to effectively integrate multimodal understanding with physical robot control.
• Challenges in scaling robotic solutions across diverse environments.
• Need for precise, real-time decision-making in robotics.

Their goals typically revolve around:

• Developing autonomous systems that can perceive, reason, and act in real-world settings.
• Improving the efficiency and adaptability of robots in various tasks.
• Staying ahead of technological advancements in AI and robotics.

Interests include advancements in AI methodologies, applications of robotics in business, and emerging technologies in multimodal AI frameworks. Communication preferences lean towards technical documentation, research publications, and informative webinars.

Bridging Perception and Action in Robotics

Multimodal Large Language Models (MLLMs) have significant potential in enabling machines such as robotic arms and legged robots to perceive their surroundings, interpret scenarios, and take meaningful actions. Integrating this intelligence into physical systems is essential for advancing robotics towards fully autonomous machines capable of planning and moving based on contextual understanding.

Limitations of Prior VLA Models

Historically, tools designed for robot control have relied on vision-language-action (VLA) models. These models convert visual observations into control signals, but often struggle with accuracy and adaptability during complex tasks. Issues include:

• Performance degradation in diverse or long-horizon robotic operations.
• Limited generalization across different environments or robot types.

Introducing VeBrain: A Unified Multimodal Framework

Researchers from Shanghai AI Laboratory, Tsinghua University, and SenseTime Research have developed VeBrain, a unified framework that reformulates robot control as text-based tasks within a 2D visual space. This alignment with how MLLMs function allows for an integrated approach to multimodal understanding, spatial reasoning, and robotic control.

VeBrain is supported by the VeBrain-600k dataset, which contains over 600,000 multimodal task samples, including robot motion and reasoning steps.

Technical Components: Architecture and Robotic Adapter

VeBrain’s architecture is built on Qwen2.5-VL and includes a specialized robotic adapter with four modules:

• The point tracker updates 2D keypoints as the robot’s view changes.
• The movement controller translates 2D key points into 3D movements by combining image data with depth maps.
• The skill executor maps predicted actions to pre-trained robotic skills.
• The dynamic takeover module monitors failures, maintaining control when necessary.

This closed-loop system enables robots to make decisions, act, and self-correct in diverse environments.

Performance Evaluation Across Multimodal and Robotic Benchmarks

VeBrain was evaluated across 13 multimodal and 5 spatial benchmarks, achieving the following 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 represents a promising advancement in embodied AI, effectively redefining robot control as a language task. This integration enables high-level reasoning and low-level action to coexist, bridging the gap between image understanding and robot execution. With strong performance metrics, VeBrain indicates a shift towards more unified, intelligent robotic systems capable of autonomous operation across diverse tasks and environments.

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