RoboBrain 2.0 - An open-source embodied brain model from Zhipu.

What is RoboBrain 2.0?

RoboBrain 2.0 is a powerful open-source embodied brain model that unifies perception, reasoning, and planning, supporting the execution of complex tasks. RoboBrain 2.0 includes two versions: 7B (lightweight) and 32B (full-scale). Based on a heterogeneous architecture, it integrates a visual encoder and a language model, supporting multiple images, long videos, and high-resolution visual inputs, as well as complex task instructions and scene graphs. The model excels in spatial understanding, temporal modeling, and long-chain reasoning, making it suitable for tasks such as robot manipulation, navigation, and multi-agent collaboration, helping embodied intelligence move from the laboratory to real-world scenarios.

Main features of RoboBrain 2.0

• Spatial understandingIt performs precise point localization, bounding box prediction, and spatial relationship reasoning based on complex instructions, supporting complex tasks in three-dimensional space.
• Time modelingIt possesses long-term planning, closed-loop interaction, and multi-agent collaboration capabilities to handle continuous decision-making tasks in dynamic environments.
• Complex ReasoningIt supports multi-step reasoning and causal logic analysis, and can generate detailed explanations of the reasoning process, thereby improving decision-making transparency.
• Multimodal input processingIt supports various input formats, including high-resolution images, multi-view input, video frames, language commands, and scene graphs.
• Real-time scene adaptationIt can quickly adapt to new scenarios, update environmental information in real time, and support dynamic task execution.

The technical principles of RoboBrain 2.0

• Language ModelIt encodes natural language instructions and scene graphs into a unified multimodal tag sequence, supporting the understanding of complex task instructions.
• Multimodal fusionIt integrates visual and linguistic information, performs long-chain reasoning through a decoder, and outputs structured plans and spatial relationships.
• Phased trainingBased on a three-stage training strategy, including basic spatiotemporal learning, embodied spatiotemporal enhancement, and inference chain training in embodied context, the model performance is gradually improved.
• Distributed training and evaluationIt uses the FlagScale distributed training framework and the FlagEvalMM evaluation framework to support large-scale training and multimodal model evaluation.

RoboBrain 2.0 project address

• Project official websitehttps://superrobobrain.github.io/
• GitHub repository: https://github.com/FlagOpen/RoboBrain2.0
• HuggingFace model library: https://huggingface.co/collections/BAAI/robobrain20-6841eeb1df55c207a4ea0036
• arXiv technical paper: https://arxiv.org/pdf/2507.02029

Application scenarios of RoboBrain 2.0

• Industrial AutomationRoboBrain 2.0 is used for complex tasks on industrial production lines, such as parts picking and assembly, welding, and painting. Through precise spatial perception and long-chain reasoning capabilities, it optimizes production processes and improves production efficiency and quality.
• Logistics and WarehousingIn logistics warehouses, robots are controlled to complete tasks such as cargo handling, sorting, and inventory management, supporting multi-agent collaboration, improving logistics efficiency, and reducing labor costs.
• Smart Home and ServicesAs the core brain of smart homes, it understands natural language commands and controls robots to complete household chores such as cleaning and tidying up rooms. It also supports home security monitoring, identifies abnormal situations in real time, and sends alarms.
• Medical RehabilitationIn rehabilitation therapy, controlling a rehabilitation robot provides personalized training programs based on the patient's rehabilitation progress, helping the patient recover physical function more quickly.
• Agricultural automationIn the agricultural sector, monitoring crop growth, identifying pests and diseases, and controlling harvesting robots for precise harvesting improves agricultural production efficiency and quality.