Google Gemini AI model brings real-time intelligence to bi-arm robots
Google DeepMind has unveiled a groundbreaking artificial intelligence model specifically designed for robotics, known as Gemini Robotics On-Device. This innovative model is capable of operating independently on a local device without the need for a continuous data connection, making it ideal for real-world applications. 
Real-time Intelligence for Bi-Arm Robots
Carolina Parada, Senior Director and Head of Robotics at Google DeepMind, introduced the Gemini Robotics On-Device model in a recent blog post. The model integrates voice, language, and action processing to enable bi-arm robots to execute complex tasks in diverse environments.
The model's key features include:
- Low-latency performance
- Operates independently of the cloud
- Suited for latency-sensitive and real-time applications
Google's new robotics AI can run without the cloud and still tie ... 
Development and Capabilities
Access to the Gemini Robotics On-Device model is currently limited to participants in Google's trusted tester program. Developers can engage with the AI system through the Gemini Robotics software development kit (SDK) and the MuJoCo physics simulator.
While Google has not disclosed detailed information about the model's architecture or training methods, it has emphasized the following capabilities:
- Designed for bi-arm robotic platforms
- Requires minimal computing resources
- Adaptable to new tasks with minimal demonstrations
Performance and Adaptability
During internal trials, the Gemini Robotics On-Device model showcased impressive performance in interpreting natural language commands and executing a variety of complex tasks. From everyday activities like folding clothes to industrial assembly tasks, the model demonstrated high levels of dexterity. 
Originally trained on ALOHA robotic systems, the model has been successfully adapted to work with other bi-arm robots such as Franka Emika's FR3 and Apptronik's Apollo humanoid robot. Google reports consistent performance across different platforms, even when faced with challenging tasks or multi-step instructions.
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