AI RESEARCH ANALYSIS
COSMOS POLICY: FINE-TUNING VIDEO MODELS FOR VISUOMOTOR CONTROL AND PLANNING
Cosmos Policy revolutionizes robot control by fine-tuning NVIDIA's advanced Cosmos-Predict2 video model. This approach enables state-of-the-art visuomotor control and planning, achieving superior performance across complex manipulation tasks in simulation and real-world environments.
Executive Impact Summary
Our analysis reveals Cosmos Policy delivers unprecedented reliability and precision for robotic operations. By leveraging advanced video models and incorporating model-based planning, enterprises can achieve significant improvements in automation success rates, reduce operational failures, and accelerate deployment of AI-driven robotics in complex industrial settings.
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LIBERO Success Rate
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Real-World Task Success
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Planning Boost in Completion
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Leveraging Video Models for Robotic Control
Cosmos Policy introduces a simple yet powerful approach to adapt large pretrained video models for complex visuomotor control. Instead of intricate multi-stage training or new architectures, it leverages "latent injection" to embed robot-specific modalities directly into the video model's diffusion process.
Enterprise Process Flow
Start with Pretrained Video Model (Cosmos-Predict2)
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Inject Robot Proprioception, Actions, Values as Latent Frames
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Single-Stage Fine-tuning on Robot Demonstration Data
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Unified Model for Policy, World Model & Value Function
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Deploy for State-of-the-Art Robot Control
Unmatched Performance Across Benchmarks
Cosmos Policy sets new state-of-the-art records in both simulation and real-world robot manipulation tasks, demonstrating superior generalization and precision compared to existing diffusion, video-based, and vision-language-action models.
| Method | Put X on Plate (Score) | Fold Shirt (Score) | Put Candies in Bowl (Score) | Put Candy in Ziploc Bag (Score) | Average Score |
|---|---|---|---|---|---|
| Cosmos Policy (ours) | 100.0 | 99.5 | 89.6 | 85.4 | 93.6 |
| π0.5 | 98.3 | 99.5 | 95.2 | 61.5 | 88.6 |
| OpenVLA-OFT+ | 68.3 | 99.5 | 21.6 | 58.5 | 62.0 |
(Data from ALOHA real-world evaluation, demonstrating Cosmos Policy's lead in challenging bimanual tasks.)
Enhancing Reliability with Model-Based Planning
Beyond direct policy execution, Cosmos Policy refines its world model and value function through policy rollout data, enabling effective model-based planning to achieve even higher success rates, especially in challenging tasks.
12.5%
Higher Task Completion with Model-Based Planning
Case Study: Precision in "Put Candy in Ziploc Bag"
The 'Put Candy in Ziploc Bag' task presents a significant challenge due to requirements for high-precision manipulation and multimodal grasp sequences. Initial attempts with the base Cosmos Policy often struggled with maintaining grip on the ziploc bag slider. However, by fine-tuning the model on policy rollout data and leveraging model-based planning, Cosmos Policy was able to predict future states more accurately and avoid these critical errors, leading to a substantial increase in success rate for this complex task.
This demonstrates the power of model-based planning in refining a robot's understanding of its environment and enabling more robust, successful task execution in the face of uncertainty and high dexterity demands.
Calculate Your Potential ROI
See how Cosmos Policy can transform your operational efficiency. Adjust the parameters below to estimate your potential annual savings and reclaimed human hours.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your Path to Advanced Robotic Automation
Our phased implementation roadmap ensures a smooth, efficient, and high-impact integration of Cosmos Policy into your enterprise operations.
Phase 01: Discovery & Strategy
Initial consultation to understand your specific robotic challenges, assess existing infrastructure, and define clear objectives for AI-driven automation. This phase includes a detailed review of Cosmos Policy's applicability to your use cases.
Phase 02: Data Integration & Model Fine-tuning
Collection and preparation of robot demonstration data. Fine-tuning of the Cosmos-Predict2 model with your proprietary data to create a tailored Cosmos Policy capable of executing your specific manipulation tasks.
Phase 03: Pilot Deployment & Optimization
Deployment of Cosmos Policy in a pilot environment. Collection of policy rollout data to refine the world model and value function, enabling enhanced model-based planning for robust and reliable performance.
Phase 04: Full-Scale Integration & Support
Seamless integration into your production environment, comprehensive training for your team, and ongoing support to ensure sustained performance and continuous improvement of your AI-powered robotic systems.
Ready to Transform Your Robotics with AI?
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