Real-World RL Environments for Physical AI

1Policy

Connect your policy. Define the test.

Upload a model checkpoint or connect an inference endpoint. Specify the task, test environment, and success criteria. SE3 uses the same evaluation protocol across test stations and policy versions, so you can compare results consistently.

• Model checkpoint or inference endpoint
• Runtime configuration
• Task and environment specification
• Success criteria

2Fleet

One evaluation. A distributed robot fleet.

SE3 deploys policies across standardized robot workcells in controlled labs, factories, and other operating environments. We handle the hardware, sensors, task setup, safety, scheduling, and execution, giving teams consistent access to real-world infrastructure without building it themselves.

3Intervention

Failures become targeted training data.

The policy runs autonomously until it fails or reaches a safety boundary. A trained operator completes the task and returns control. SE3 preserves the full transition as a continuous correction trajectory.

4Data

Convert robot rollouts into annotated training datasets.

Raw recordings become synchronized, quality-controlled datasets, segmented by task and outcome and returned in one consistent format.

1. 1Synchronize sensor and video timelines
2. 2Automated QA/QC
3. 3Segment tasks and outcomes
4. 4Language annotation
5. 5Hand and action tracking
6. 6Package training-ready data

5Iterate

Train. Evaluate. Improve. Repeat.

Compare checkpoints on the same tasks and environments. SE3 measures what improved, identifies the remaining failure distribution, and turns those failures into targeted data for the next training cycle.