DebuggingRobot LearningAt Scale

We make robots work by evaluating robots in real world tasks virtually and repeatably.

Robot learning has no debugger. Teams can see when a robot drops a cup, misses a handoff, or fails to recover. What they cannot quickly determine is whether the failure came from missing data, bad annotations, an eval gap, a policy change, or sim to real.

The evidence is scattered across the robot learning stack, so every failure becomes a manual investigation. Teams spend weeks guessing what to collect, relabel, retrain, or redesign.

A failure happens in the real world.

The debugging and evaluation layer for robot learning

Run Robotics closes the evaluation loop for robot learning.Without Run, a data pipeline has a slow feedback cycle and reaches production with no evaluations. With Run, data and repeatable simulation evaluations move through a faster feedback cycle before production.
Without Run RoboticsOpen loop
Data pipelineQuality ↓
No virtual evalsSlow feedback
ProductionRepeated failures
With Run RoboticsClosed loop
Data pipelineQuality ↑
Run evalsSim / world model
Grapes into grey box
Lego into Lego bag
Brown bar into top pocket
Toy jeep into grey box
Teacup into Lego bag
Quick, grab the red block
Close the laptop
Grab the bottle
Sort grapes with polar bear
ProductionFewer failures

Rollout lineage

Connect every behavior to the exact data, annotations, evals, and policy that produced it.

Failure clustering

Group recurring failures so repeated symptoms become one tractable engineering problem.

Simulation evals

Replay real failures as durable, repeatable digital tests.

Regression analysis

Compare versions and see where performance actually moved.

Cause ranking

Surface the pipeline changes most likely to explain an improvement or regression.

Deployment control

Run in the Run Robotics cloud or inside the customer’s cloud.

Chase Brignac, cofounder of Run Robotics
We are starting with robotics data quality.
Initial wedgeConnect datasets to measurable evaluation outcomes