%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
ROCKET 1
Overview :
ROCKET-1 is a Visual-Language Model (VLM) specifically designed for embodied decision-making in open-world environments. This model connects VLMs with policy models through a visual-temporal context prompting protocol, guiding policy-environment interactions using object segmentation from past and current observations. By this means, ROCKET-1 unlocks the visual-language reasoning capabilities of VLMs, enabling it to solve complex creative tasks, especially in spatial understanding. Experiments with ROCKET-1 in Minecraft demonstrate that this approach allows agents to accomplish previously unattainable tasks, highlighting the effectiveness of visual-temporal context prompting in embodied decision-making.
Target Users :
The target audience includes AI researchers, game developers, and developers of multimodal learning models. ROCKET-1 is well-suited for these individuals as it offers an advanced framework for researching and developing agents capable of making embodied decisions in complex environments, particularly in scenarios requiring spatial understanding and creative problem-solving skills.
Use Cases
In Minecraft, the agent successfully placed an oak door in a specific location using ROCKET-1.
The agent hunted a cow without touching a sheep by utilizing ROCKET-1.
The agent mined emeralds and coal in Minecraft with ROCKET-1.
Features
? Visual-Temporal Context Prompting: Guides policy-environment interactions using object segmentation from past and current observations.
? Causal Transformer: Processes interaction types, observations, and object segmentation to predict actions.
? Real-time Object Tracking: Provided by SAM-2, enhances the model's interaction capabilities.
? Integration with Advanced Reasoners: The GPT-4o model and Molmo model work in conjunction to break down complex tasks into steps.
? Zero-Shot Generalization Capability Assessment: Minecraft interaction benchmarks are designed to evaluate the model's generalization abilities.
? Diverse Task Solving: Completes a variety of complex and creative tasks in Minecraft.
? Interaction Type Diversity: Supports six types of interactions in Minecraft, totaling 12 tasks.
How to Use
1. Visit the ROCKET-1 GitHub page to access the code and documentation.
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%230080ff;%20}%20%3c/style%3e%3c/defs%3e%3cpath%20class='st0'%20d='M16.2,11.1c.4.5.4,1.2,0,1.8l-4.7,7.1h-3.8l5.3-8L7.6,4h3.8l4.7,7.1Z'/%3e%3c/svg%3e)
2. Read and understand how ROCKET-1 works and the visual-temporal context prompting protocol.
3. Set up the development environment and install necessary dependencies following the documentation guidelines.
4. Run the ROCKET-1 model and conduct tests in a Minecraft environment.
5. Interact with ROCKET-1 using the Gradio platform to experience its decision-making abilities.
6. Adjust model parameters as needed to optimize performance.
7. Explore potential applications of ROCKET-1 in other open-world environments.
Featured AI Tools
Tensorpool
TensorPool is a cloud GPU platform dedicated to simplifying machine learning model training. It provides an intuitive command-line interface (CLI) enabling users to easily describe tasks and automate GPU orchestration and execution. Core TensorPool technology includes intelligent Spot instance recovery, instantly resuming jobs interrupted by preemptible instance termination, combining the cost advantages of Spot instances with the reliability of on-demand instances. Furthermore, TensorPool utilizes real-time multi-cloud analysis to select the cheapest GPU options, ensuring users only pay for actual execution time, eliminating costs associated with idle machines. TensorPool aims to accelerate machine learning engineering by eliminating the extensive cloud provider configuration overhead. It offers personal and enterprise plans; personal plans include a $5 weekly credit, while enterprise plans provide enhanced support and features.
Model Training and Deployment
308.3K
Scireviewhub
SciReviewHub is an AI-powered tool designed to accelerate scientific writing and literature reviews. We leverage AI technology to quickly filter relevant papers based on your research goals and synthesize the most pertinent information into easily understandable and readily usable literature reviews. Through our platform, you can enhance your research efficiency, expedite publication timelines, and achieve breakthroughs in your field. Join SciReviewHub and reshape the future of scientific writing!
Research Tools
285.9K