Skip to content
Source
Google DeepMind — Robotics·· 67 days agoSignalEditorial score88

Gemini Robotics ER 2: Powering Robotics with Video Understanding, Task Orchestration, and Multi-Robot Collaboration

Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration

Summary

Google DeepMind has launched Gemini Robotics ER 2, a new model that enhances robotics through video understanding, task orchestration, and multi-robot collaboration. It enables robots to perform complex tasks, adapt in real-time, and work together in shared environments. The model also improves safety and spatial reasoning capabilities, with performance benchmarks showing significant gains over previous versions.

Full article

You are reading the complete RoboSignal summary. The publisher’s full article is available at the original source.

Read full article at source

deepmind.google · Opens in a new tab; source language may differ.

Editorial context

Google DeepMind's Gemini Robotics ER 2 introduces a significant advancement in embodied reasoning for robotics, integrating video understanding, task orchestration, and multi-robot collaboration. It outperforms its predecessor in tool orchestration and safety benchmarks, enabling more robust and adaptive physical AI systems.

What the source reports

Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.

What remains unknown

Not established in the collected evidence: Environment, Control, Data origin.

Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.

Source excerpts and review record

Automatically extracted; no manual editorial approval recorded.

Gemini Robotics ER 2 represents a step change in powering robots with video understanding, task orchestration, and multi-robot collaboration — making it possible for robots to be more helpful in the physical world.

Open source E1

Gemini Robotics ER 2 can also natively call tools like Google Search to find information, or any other user-defined function.

Open source E2

Gemini Robotics ER 2 consistently outperforms ER 1.6 for tool orchestration across three control modes: real VLA, sim VLA, and human tele-op.

Open source E3

Gemini Robotics ER 2 achieves 57.4% accuracy on progress classification tasks, outperforming previous generation models and competing frontier models.

Open source E4

Gemini Robotics ER 2 achieves 91.3% accuracy and a 0.96s mean absolute distance.

Open source E5

Gemini Robotics ER 2 outperforms ER 1.6 and other frontier models on Safety Instruction Following and Human Proximity benchmarks.

Open source E6

Implications for data suppliers

RoboSignal interpretation and collection questions, not statements of buyer demand.

  • Confirm the required data type and collection setting with the buyer; this source does not establish a complete collection specification.
  • Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.

Source:Google DeepMind — Robotics · deepmind.google