VLA Evaluation & Robot Policy Benchmarking
Real-robot evaluation run at trial counts that can support a conclusion, with the protocol documented, failures classified, recovery measured and performance anchored to a human baseline on the same fixture.
What VLA Evaluation Measures
| Question | Evidence |
|---|---|
| How capable is it? | Success on trained tasks |
| How does it fail? | Failure taxonomy and recovery |
| How far does it generalise? | Novel objects, scenes, instructions and initial conditions |
Why Two Labs Get Different Numbers From the Same Model
A number without its complete action, state, preprocessing, reset and fixture configuration is not a reproducible result.
How Many Rollouts You Actually Need
| Claim | Method |
|---|---|
| Estimate one success rate | Choose a target confidence interval |
| Compare two policies | Power for the smallest useful difference |
| Choose an architecture | Use powered trials or a distributional protocol |
Success Rate Hides How a Policy Fails
Recovery is reported beside success, with calibrated failure classes and per-trial evidence.
The Summary Statistic Is a Methodological Commitment
The statistic is agreed before the run and reasonable alternatives are reported.
Testing Transfer, Not Memorisation
Generalisation is separated across object, scene, instruction and initial-condition axes.
Simulation and Hardware Answer Different Questions
Simulation supports regression and coverage. Hardware supports claims about physical performance.
Anchoring to a Human Baseline
Human demonstration operators run the same fixture and protocol.
How an Evaluation Runs
- Protocol design
- Fixture and reset calibration
- Operator calibration
- Pilot cell
- Trial execution
- Human baseline
- Analysis and report
Where VLA Evaluation Goes Wrong
Underpowered trial counts, missing intervals, unrecorded configuration, post-hoc statistics and unstated success criteria are prevented at protocol lock.
Related Robotics Services
Human Demonstrations Multimodal Sensor Data 3D & Spatial Annotation AI Model Testing
VLA Evaluation FAQs
What is VLA evaluation?
Controlled measurement of robot-policy capability, failure behaviour, recovery and generalisation using a documented protocol and a trial count that supports the claim.
How many rollouts do we need?
There is no universal count. The count is derived from the decision, effect size and pilot-observed variance, with uncertainty stated beside every result.
Why do different labs report different numbers for the same model?
Action representation, state source, preprocessing, control rate, reset procedure and success criteria can materially change the result when they are not documented consistently.
Why does recovery rate matter alongside success rate?
Policies with similar success can behave very differently after failure. Recovery distinguishes retries, loops and genuine correction.
What failure modes do you classify?
The taxonomy begins with grasp instability, repetition loops, state mismatch and precision misalignment, then extends to the client's tasks.
Can we just use benchmark leaderboard results?
Leaderboards track the field, but they do not establish performance on the client's robot, fixture and operating assumptions.
Should we evaluate in simulation or on real hardware?
Simulation supports regression and coverage; hardware supports physical performance claims. Where both run, the gap is reported by task class.
What is a human baseline and why would we want one?
Trained operators run the identical fixture and protocol so policy performance has a practical scale and fixture constraints become visible.
Why does the choice of summary statistic matter?
Different reasonable summaries can reverse a ranking when performance distributions cross, so the statistic is agreed before trials and alternatives are reported.
How is this different from deployment validation?
VLA evaluation compares policy capability under controlled conditions; deployment validation checks a specific system in a specific operational place before go-live.