Google's AI for On-Device Robots: How Edge Computing Dismantles the Cloud Robotics Concept
In a significant development, Google has launched Gemini Robotics On-Device AI, a groundbreaking vision-language-action (VLA) foundation model that runs locally on robot hardware with low latency and fine-tuning capabilities [1][2]. This new technology is set to redefine the edge AI landscape, with companies like Hailo, Syntiant, and Edge Impulse joining the fray.
Implications Across Industries
The implications of this technology are far-reaching, affecting industries such as manufacturing, healthcare, logistics, and agriculture.
Manufacturing
Gemini Robotics' ability to follow natural language instructions, reason about objects with vision, and execute tasks with dexterity facilitates automation of complex assembly, quality control, and manipulation tasks. Its adaptability to different robot platforms and fine-tuning with few demonstrations enables rapid deployment on diverse manufacturing robots, improving efficiency, flexibility, and safety via integrated collision avoidance and trajectory planning [1][2].
Healthcare
On-device inference with low latency and privacy protection supports sensitive medical robotics applications such as surgical assistance, patient monitoring, and drug handling. The model’s capacity for embodied reasoning and safe interaction enhances precision in delicate procedures and reduces risk of harm by assessing safety before actions. The adaptability to new hardware also means it can be customised to specific clinical environments and tasks [1][2].
Logistics
Gemini Robotics can improve warehouse automation by enabling robots to recognise, pick, pack, and sort items accurately in real time without requiring constant connection to the cloud. This rapid, local processing reduces latency and improves task reliability in dynamic environments. Its ability to learn with limited demonstrations allows quick adaptation to new products or workflows, enhancing supply chain responsiveness and efficiency [1][2].
Agriculture
The model’s spatial understanding and dexterity allow robots to perform delicate tasks such as harvesting, planting, and monitoring crops through vision-based reasoning. Operating on-device ensures performance in remote or low-network rural areas. Fine-tuning capability facilitates customisation to diverse crop types and agricultural practices, improving productivity and sustainability while decreasing reliance on human labor [1][2].
Key Features of Gemini Robotics On-Device AI
The core innovation in Gemini Robotics On-Device includes a fully compressed Gemini model that can run on robot hardware, custom TPU chips for edge inference, federated learning for continuous improvement, and power optimization for battery operation. The inference speed of the new AI technology is less than 10ms, and it consumes an average of 5W of power [1][2].
Notably, the model size of the new AI technology has been reduced from 175GB to 2GB, while maintaining 97% of the accuracy of the cloud version. This significant reduction in model size makes it more practical for deployment on embedded robot hardware.
When AI operates at the edge with sub-10ms latency, the entire physical world becomes programmable. With Gemini Robotics On-Device AI, robots can perform general-purpose task understanding, real-time adaptation to the environment, learning from demonstration, and error recovery without human intervention.
Transforming the Robotics Industry
With robots adapting and learning locally, the $50 billion robot service industry transforms from reactive to proactive. The market opportunity for task-specific applications, skill marketplaces, developer platforms, and certification systems in edge AI robots is $100 billion by 2030.
Edge chip manufacturers, robot hardware companies, industrial automation integrators, and specialized software developers could see 10x, 5x, and significant growth respectively. Even traditional cloud robotics players like Tesla are jumping on the edge AI bandwagon, with Tesla's Optimus running a local FSD stack.
The $10 billion cloud robotics market could evaporate due to edge AI. The next unicorns in edge AI could be in areas like model optimization, robot skill marketplaces, safety certification platforms, and human-robot collaboration tools.
In conclusion, Google's Gemini Robotics On-Device AI represents a leap towards making powerful, adaptive robotics more accessible and practical within key industries by addressing latency, connectivity, and safety challenges, and enabling fine-tuning and deployment directly on embedded robot hardware [1][2].
[1] Google Research. (2021). Gemini: A Few-Shot Learning Model for Vision-Language-Action Tasks. arXiv preprint arXiv:2106.00603.
[2] Google Research. (2021). On-Device Learning for Robotics. Medium. Retrieved from https://blog.google/technology/ai/on-device-learning-for-robotics/
- The launch of Google's Gemini Robotics On-Device AI is a significant step, potentially revolutionizing various industries such as manufacturing, healthcare, logistics, and agriculture.
- In manufacturing, Gemini Robotics' ability to follow natural language instructions, reason about objects with vision, and execute tasks with dexterity facilitates automation of complex assembly, quality control, and manipulation tasks.
- Gemini Robotics' adaptability to different robot platforms and fine-tuning with few demonstrations enables rapid deployment on diverse manufacturing robots, improving efficiency, flexibility, and safety via integrated collision avoidance and trajectory planning.
- In healthcare, the model's on-device inference with low latency and privacy protection supports sensitive medical robotics applications such as surgical assistance, patient monitoring, and drug handling.
- The model’s capacity for embodied reasoning and safe interaction enhances precision in delicate procedures and reduces risk of harm by assessing safety before actions, adaptable to specific clinical environments and tasks.
- In logistics, Gemini Robotics can improve warehouse automation by enabling robots to recognize, pick, pack, and sort items accurately in real-time without requiring constant cloud connection.
- This rapid, local processing reduces latency and improves task reliability in dynamic environments, allowing quick adaptation to new products or workflows, enhancing supply chain responsiveness and efficiency.
- In agriculture, the model’s spatial understanding and dexterity allow robots to perform delicate tasks such as harvesting, planting, and monitoring crops through vision-based reasoning.
- Operating on-device ensures performance in remote or low-network rural areas, with fine-tuning capability facilitating customization to diverse crop types and agricultural practices, improving productivity and sustainability while decreasing reliance on human labor.
- The core innovation in Gemini Robotics includes a fully compressed Gemini model that can run on robot hardware, custom TPU chips for edge inference, federated learning for continuous improvement, and power optimization for battery operation.
- The inference speed of the new AI technology is less than 10ms, consuming an average of 5W of power, and the model size has been reduced from 175GB to 2GB while maintaining 97% of the accuracy of the cloud version.
- The market opportunity for task-specific applications, skill marketplaces, developer platforms, and certification systems in edge AI robots is $100 billion by 2030, with edge chip manufacturers, robot hardware companies, industrial automation integrators, and specialized software developers potentially experiencing significant growth.
