Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning (2503.15558v3)

Abstract: Physical AI systems need to perceive, understand, and perform complex actions in the physical world. In this paper, we present the Cosmos-Reason1 models that can understand the physical world and generate appropriate embodied decisions (e.g., next step action) in natural language through long chain-of-thought reasoning processes. We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning. To represent physical common sense, we use a hierarchical ontology that captures fundamental knowledge about space, time, and physics. For embodied reasoning, we rely on a two-dimensional ontology that generalizes across different physical embodiments. Building on these capabilities, we develop two multimodal LLMs, Cosmos-Reason1-7B and Cosmos-Reason1-56B. We curate data and train our models in two stages: Physical AI supervised fine-tuning (SFT) and Physical AI reinforcement learning (RL). To evaluate our models, we build comprehensive benchmarks for physical common sense and embodied reasoning according to our ontologies. Evaluation results show that Physical AI SFT and RL bring significant improvements. To facilitate the development of Physical AI, we make our code and pre-trained models available under the NVIDIA Open Model License at https://github.com/nvidia-cosmos/cosmos-reason1.

Insights on Cosmos-Reason1 Models for Physical AI Reasoning

The exploration of Artificial Intelligence's (AI) capabilities to perceive, understand, and interact with the physical world is central to advancing Physical AI systems. The paper "Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning" explores this field by introducing the Cosmos-Reason1 models that aim to amplify AI's ability to reason through perceived data to generate context-driven decisions in natural language.

Model Architecture and Capabilities

The Cosmos-Reason1 models are distinguished by their focus on understanding the physical world through multimodal inputs, primarily utilizing visual data in the form of video. The design leverages a hybrid Mamba-MLP-Transformer architecture, aligning with leading advancements in sequence modeling while efficiently handling long-context inputs. Two models, Cosmos-Reason1-8B and Cosmos-Reason1-56B, are presented, incorporating a vision encoder alongside text-based transformer backbones, optimized for robust handling of visual linguistic data.

Key features of the models include:

• Hierarchical Ontologies: These were crafted to fundamentally categorize knowledge—space, time, and fundamental physics—each further subdivided to encapsulate detailed understanding necessary for physical common sense.
• Embodied Reasoning: These capabilities were particularly noted as crucial for physical interaction, covering a spectrum from processing sensory data to predicting actions' effects, and respecting physical constraints.

Through rigorous training phases, including vision pre-training, supervised fine-tuning, and reinforcement learning, the models are sculpted to acquire intricate reasoning skills.

Evaluation and Benchmarking

A set of benchmarks was created to evaluate the Cosmos-Reason1 models' performance on physical common sense and embodied reasoning tasks. Key findings include:

• Significant Improvements: The inclusion of specialized supervised fine-tuning datasets markedly enhanced the models' reasoning abilities. Moreover, reinforcement learning contributed further gains, especially in handling complex queries that required intuitive physics principles.
• Comparison with Other Models: Cosmos-Reason1 models demonstrated superior performance against leading alternatives like GPT-4o, Gemini 2.0, and Qwen2.5-VL, indicating the efficacy of model architectures and training approaches in improving multimodal reasoning.

Implications and Future Directions

This initiative is poised to make significant contributions to theoretical and practical domains:

• Theoretical Advancement: Establishing robust ontologies for the physical world sets a foundation for developing sophisticated AI cognizance in real-world applications.
• Practical Impact: Enhanced physical common sense and embodied reasoning capabilities pave the way for deploying AI in varied domains, such as autonomous vehicles and robotic interactions, promising more intuitive machine interactions.
• Future Developments: The paper suggests the potential growth in AI's interaction capabilities with dynamic environments, advocating for continued research into RL mechanisms that further refine reasoning aptitudes.

Conclusion

The Cosmos-Reason1 models represent a cultivated step forward in the domain of Physical AI, emphasizing structured reasoning and interaction capabilities grounded in comprehensive multimodal data understanding. The open-source release of this project underlines NVIDIA's commitment to advancing AI's proficiency in perceiving and reasoning about the physical world, setting a prominent cornerstone future developments should build upon.