Gemini Robotics generalization benchmark
Robot policies run 85 real bi-arm manipulation tasks. About 80% test generalization to new scenes, new wording of instructions or new actions. Scored by average progress and success rate.
Of the 85 tasks, 20% are in distribution, 28% visual, 28% instruction and 24% action generalization. The Gemini Robotics On-Device cards report scene, instruction and action generalization in the same way.
- Made by
- Google DeepMind
- Released
- March 2025
- Kind
- Real robots
- Level
- Everyday
- Scored
- Settled by events
- Answers
- Agent task
- Tasks
- Kept private
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for Gemini Robotics generalization benchmark yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More in Robotics
- ERQA In use 400 multiple-choice visual questions from robot and egocentric footage on spatial, trajectory, action and state reasoning. Accuracy.
Gemini Robotics ER 2 78.5% - RoboDojo In use Generalist manipulation policies run 42 Isaac Sim tasks and 18 remote real-robot tasks. Scored by task score and success rate. GPT-6 Astra 29%
- RoboLab In use Zero-shot DROID Franka policies in Isaac Lab on 120 tabletop tasks at three instruction levels, 10 episodes each. Success rate. FLUX 3 Action 42.9%
- Progress classification (Google DeepMind) In use Models assign each frame of a robot video to one of five task-progress bands from 0 to 100 percent. Scored by accuracy. Gemini Robotics ER 2 57.4%
- Human proximity (Google DeepMind robotics) In use Models judge from robot camera views whether a human is within 1 meter, a spatial awareness check for safe robot behavior. Scored by accuracy. Gemini Robotics ER 2 93%
- Moment finding (Google DeepMind) In use Models find the moment in a robot video when an event happens. Scored by accuracy and mean absolute time distance in seconds. Gemini Robotics ER 2 91.3%