MolmoAct 7B: The New Open-Source AI Model That Teaches Robots to Reason in 3D

August 18, 2025

By Silo AI Team

MolmoAct 7B: The New Open-Source AI Model That Teaches Robots to Reason in 3D

Introduction

The field of robotics has made significant strides in recent years, with advancements in artificial intelligence (AI) models that enable robots to perform complex tasks with increased precision and efficiency. One of the most notable developments in this area is MolmoAct 7B, an open-source AI model developed by the Allen Institute for AI (Ai2) that allows robots to reason and act in 3D environments. In this article, we will delve into the features and capabilities of MolmoAct 7B, exploring how it is revolutionizing the field of embodied AI.

The Core Innovation of MolmoAct 7B

The core innovation of MolmoAct 7B lies in its ability to transform 2D image inputs into 3D spatial plans through the generation of visual reasoning tokens. This allows robots to not only "see" their surroundings but also to understand spatial and temporal relationships, and to plan movements accordingly. This results in more intelligent and adaptable navigation and manipulation in the physical world.

Key Features and Context

MolmoAct 7B boasts several key features that set it apart from other robotic AI models:

Open-source and transparent: Unlike many robotic AI models that rely on closed, proprietary datasets and architectures, MolmoAct 7B is trained entirely on open data and is designed for transparency and real-world generalization.
Training data: The model was trained on a curated dataset of about 12,000 real-world "robot episodes" from environments such as kitchens and bedrooms, focusing on practical, embodied tasks.
Step-by-step visual reasoning: MolmoAct 7B produces visual reasoning traces, making its behavior interpretable and allowing human operators to preview, steer, and adjust robot plans in real-time.
Embodied reasoning: Instead of first reasoning through language and then translating that into actions, the model directly infers actions from visual perception and spatial understanding, more closely mirroring how humans interact with their environments.
Human-AI collaboration: The system enables safer and more effective collaboration between humans and robots, as its real-time previews and adjustments improve both transparency and control.

Significance and Comparison

MolmoAct 7B represents a paradigm shift in embodied AI, moving away from black-box, language-centric models toward an open, visual, and spatial reasoning approach that aligns more closely with human cognitive processes and practical robotics needs. In comparison to previous models, MolmoAct 7B offers several advantages, including:

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Open-source and transparent architecture
Visual + spatial reasoning modality
Step-by-step visual traces for transparency and control
Real-time adjustment and generalizability
12,000+ real robot episodes as training data

Conclusion

MolmoAct 7B is a groundbreaking AI model that is poised to revolutionize the field of embodied AI. Its open-source and transparent architecture, combined with its ability to reason and act in 3D environments, makes it an ideal solution for a wide range of applications, from robotics and manufacturing to healthcare and beyond. As the field of AI continues to evolve, MolmoAct 7B is an important step forward, demonstrating the potential for AI models to learn from and interact with the world in a more human-like way.

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