Failure Diagnosis
Understand where and under which conditions a policy or model fails.
Solutions
From model failure to
real-world improvement.
EthSeq helps Physical AI teams understand why models fail, identify the experiences that matter, and turn evidence into the next data decision.
Discuss a validation
What we help with
Better evidence. Better next
decisions.
Instead of asking one data blindly, we help teams move from observed failure to controlled diagnosis, targeted experience generation, and validation.
Causal Analysis
Use controlled interventions to distinguish true factors from surface correlations.
Next-Data Decision
Translate a DataGap hypothesis into a bounded, collectable DataPlan.
Validation Loop
Retrain, re-measure, and verify whether the intervention actually improves the failure.
Workflow
One failure. One controlled
loop.
The engagement stays narrow and measurable so a team can test value before broader integration.
Bring a failure case
Model or checkpoint, task, failure trace, evaluation protocol, and the evidence available.
Run interventions
Create controlled branches that alter one factor while preserving a comparable anchor.
Attribute the failure
Compare outcomes to form a bounded, testable DataGap hypothesis.
Generate DataPlan
Specify which new experiences are worth collecting next and how they will be measured.
Validate
Retrain and compare against held-out evaluation before claiming an outcome.
Who it’s for
Teams building embodied
intelligence.
Robot Learning Teams
Diagnose policy failures and prioritize the next experience.
VLA / Embodied AI
Separate surface correlations from physical, task-relevant factors.
AI Research Labs
Run controlled experiments on causal understanding and robust generalization.
Simulation & Data Teams
Connect model feedback to more intentional collection protocols.
Design partner model
Start from a real failure, not a broad platform rollout.
EthSeq can work with evaluation traces and essential model outputs while keeping sensitive training data inside the partner environment.
Have a failure case worth understanding?
Start with a small joint validation or technical discussion.