Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Google DeepMind

Google DeepMind is a world-renowned artificial intelligence research laboratory, formed from the merger of DeepMind and Google's AI division. It stands at the forefront of AI innovation, responsible for groundbreaking advancements, including the development of the Gemini series of multimodal AI models and the Gemma open-model family. DeepMind's mission is to solve intelligence to advance science and benefit humanity.

General
AuthorGoogle DeepMind
Release Date2010 (DeepMind founding)
Websitehttps://deepmind.google/
Technology TypeAI Research Organization

Key Research Areas and Achievements

  • Reinforcement Learning: Pioneering work in reinforcement learning, including AlphaGo, which defeated world champions in Go.
  • Large Language Models: Development of advanced LLMs, contributing to the Gemini and Gemma model families.
  • Scientific Discovery: Application of AI to accelerate scientific research, such as AlphaFold for protein structure prediction.
  • Safety and Ethics: Dedicated research into AI safety, ethics, and responsible deployment.

Start Exploring Google DeepMind

Google DeepMind's research underpins many of the most advanced AI systems in the world. As the organization behind foundational models like Gemini and Gemma, its work is crucial for understanding the future of AI. Developers and researchers can delve into their publications and open-source contributions to gain insights into cutting-edge AI development.

👉 DeepMind Research Publications 👉 About Google DeepMind

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Google deepmind AI technology Hackathon projects

Discover innovative solutions crafted with Google deepmind AI technology, developed by our community members during our engaging hackathons.

Adaptifleet

Adaptifleet

Traditional warehouse automation has improved efficiency, yet many systems remain rigid, expensive, and difficult to adapt when workflows or layouts change. Even small adjustments often require specialized expertise or time-consuming reprogramming. This creates a disconnect between what operators need robots to do and how easily they can communicate those needs — a challenge we call the “Human Intent Gap.” AdaptiFleet was designed to close this gap by enabling intuitive, AI-driven fleet control. Instead of relying on complex interfaces or predefined scripts, users interact with autonomous robots using natural language. Commands such as “Get me three bags of chips and a cold drink” are interpreted and translated into structured robotic tasks automatically. At its core, AdaptiFleet leverages Gemini-powered Vision Language Models (VLMs) to understand user intent and visual context. Robots operate within a dynamic decision framework, allowing them to adapt to changing environments rather than follow rigid, pre-programmed routes. The platform integrates a digital twin simulation stack built on Isaac Sim, enabling teams to validate behaviors, test workflows, and optimize multi-robot coordination before live deployment. Once deployed, ROS2 and Nav2 provide robust navigation, dynamic path planning, and collision avoidance. The VLM orchestration layer continuously analyzes visual inputs to support scene understanding, anomaly detection, and proactive hazard awareness. When conditions change, AdaptiFleet autonomously re-plans routes and tasks, reducing downtime and operational disruption. By combining conversational interaction, real autonomy, and simulation-driven validation, AdaptiFleet simplifies robotic deployments while improving efficiency and visibility. The result is an automation system that is adaptive, scalable, and aligned with how people naturally work.

RoboGripAI

RoboGripAI

This project presents a simulation-first robotic system designed to perform structured physical tasks through reliable interaction with objects and its environment. The system focuses on practical task execution rather than complex physics modeling, ensuring repeatability, robustness, and measurable performance across varied simulated conditions. Simulation-first robotic system performing structured physical tasks such as pick-and-place, sorting, and simple assembly. Designed for repeatable execution under varied conditions, with basic failure handling, environmental interaction, and measurable performance metrics. A key emphasis of the system is reliability under dynamic conditions. The simulation introduces variations such as object position changes, minor environmental disturbances, and task sequence modifications. The robot is designed to adapt to these variations while maintaining consistent task success rates. Basic failure handling mechanisms are implemented, including reattempt strategies for failed grasps, collision avoidance corrections, and task state recovery protocols. The framework incorporates structured task sequencing and state-based control logic to ensure deterministic and repeatable behavior. Performance is evaluated using clear metrics such as task completion rate, execution time, grasp accuracy, recovery success rate, and system stability across multiple trials. The modular system design allows scalability for additional tasks or integration with advanced planning algorithms. By prioritizing repeatability, robustness, and measurable outcomes, this solution demonstrates practical robotic task automation in a controlled simulated environment, aligning with real-world industrial and research use cases. Overall, the project showcases a dependable robotic manipulation framework that bridges perception, decision-making, and action in a simulation-first setting, delivering consistent and benchmark-driven task execution.