In the age of intelligent machines, edge AI is quickly becoming the gold standard for robotics performance. Google DeepMind’s latest platform, Gemini Robotics On-Device, is shaping this shift. It promises local computation, low latency, enhanced privacy, and robust adaptability for real-world tasks.

Although Gemini Robotics On-Device is not fully open-source, its semi-open ecosystem echoes Google’s Android strategy. Through limited SDK access and a trusted tester program, Gemini allows developers to integrate its powerful models with selected robot hardware. This unlocks massive potential for companies like Reeman, a rising robotics manufacturer in the fields of autonomous logistics and service automation.

Gemini On-Device: A Semi-Open Ecosystem for Embodied AI

According to DeepMind’s official blog post (https://deepmind.google/discover/blog/gemini-robotics-on-device-brings-ai-to-local-robotic-devices/), Gemini On-Device uses a closed-core VLA (Video-Language-Action) model. However, developers can request access through the Trusted Tester Program. Once approved, they gain tools to deploy the model on local devices, test performance using MuJoCo simulators, and integrate with hardware like Apollo humanoids or Franka Emika arms.

This strategy is similar to early Android development. While core model weights remain protected, surrounding tools like SDKs and adapters offer enough flexibility for ecosystem growth.

How Reeman Products Fit In

Reeman develops various types of autonomous machines, including:

• Autonomous forklifts for warehouse logistics;
• Commercial cleaning robots for retail and industrial floors;
• Delivery robots for last-mile indoor transport.

These robots are already equipped with sensors, LIDAR, and SLAM navigation. However, integrating with Gemini On-Device could take their capabilities to a new level:

• Faster response times due to local decision-making;
• Task generalization with only 50–100 demonstrations per function;
• Adaptive performance in semi-structured or unpredictable environments.

For example, a Reeman forklift could use Gemini to recognize object types, interpret verbal commands, and avoid obstacles more intelligently—all without relying on cloud servers.

Cross-Platform Compatibility and Future Potential

Gemini has already been validated with diverse robots: Google ALOHA, Franka FR3, and Apptronik Apollo. This demonstrates the architecture’s support for heterogeneous systems. According to DeepMind, future expansions will include platforms like Boston Dynamics and more.

Reeman could apply for integration early, ensuring its products are part of the evolving Gemini ecosystem. Such alignment can increase product longevity, software compatibility, and AI competitiveness.

Related Projects: The Case of ALOHA 2

For those seeking more open alternatives, ALOHA 2 (https://aloha-2.github.io/) provides a fully open-source bimanual robot platform. Developed by Google and Stanford, ALOHA 2 includes 3D CAD files, control software, and imitation learning demos.

Still, for companies looking for state-of-the-art model performance with limited integration barriers, Gemini Robotics On-Device offers a practical, powerful middle ground.

Conclusion: Reeman and the Edge AI Revolution

Edge computing is redefining robotics. DeepMind’s Gemini Robotics On-Device shows that intelligence no longer needs to live in the cloud. Reeman, with its real-world hardware platforms and mature navigation stacks, stands as a strong candidate to benefit from this shift.

By adopting Gemini’s SDK, Reeman could enhance robot performance, broaden task capabilities, and future-proof its product line. As Gemini expands support for new brands and devices, proactive integration today can secure a leadership role in tomorrow’s AI-driven robotics landscape.

For more details, read the official DeepMind blog.