Use failures to make
robots more reliable.
The autonomy infrastructure that takes robots from demo to reliable, self-improving deployment.
Start with the runs your team already records. Bring the failed attempts and the policy behind them.
Your failed runs.
The next
training job.
Bring your policy and the failures you already collect. Runs connects those cases to targeted corrective demonstrations and a fine-tuned candidate.
Start with one repeatable failure. Train on the corrections alongside successful examples, then put the candidate through your existing evaluation workflow.
Your current policy
The model you want to improve.
Your failed runs
Logs, recordings and examples of the failure.
The task and conditions
What the robot attempts, and where it struggles.
The result that matters
Define what a better run should look like.
From a failed attempt.
To a fine-tuned policy.
Runs orchestrates six steps from failed runs to corrective data and policy fine-tuning. Evaluate candidates in your existing simulation environment, then bring improvements back to deployment.
Bring the attempts
that need to improve.
Start with logs, recordings and failure examples from your existing workflow. Include the current policy and the task it was trying to complete.
Your policy + recorded failures + task context
OUTPUTThe failed cases to target in training
Your policy.
Your simulator.
A better next run.
Runs focuses on corrective data and policy fine-tuning. Bring failures from your existing logging workflow and test the candidate in the environment your team already uses.
Your team or an evaluation partner runs the tests. You decide which candidate returns to the robot.
Bring the failure you need to fix.
Start with one task, your current policy and representative runs. And watch your deployments become more reliable, with less effort.