Evaluation And Release Guides
Robotics releases need stronger evidence than aggregate training metrics. These guides focus on the surfaces and habits that make release decisions credible: replay, benchmark coverage, scenario-aware comparison, promotion rules, and post-deploy validation.
Release Evidence Components
Benchmark packs
Scenario collections that represent what a release must handle reliably.
- representative success cases
- known hard negatives
- historically important regressions
- customer- or site-specific critical scenarios
Golden episode libraries
Stable reference cases that should not regress across model generations.
Replay suites
Candidate and baseline comparison against the same stored evidence wherever replay is appropriate.
Post-deploy validation windows
Canary or staging observation periods that turn early field behavior into part of the release record.
Release Questions The Platform Should Answer
- What exact benchmark pack approved this release?
- Which scenarios improved and which regressed?
- What latency, intervention, or rollout thresholds applied?
- Who approved promotion and why?
- What happened in the first real deployment cohort afterward?
These questions should be answerable without leaving the platform or reconstructing context manually.
High-Value Evaluation Habits
Evaluate by scenario, not only averages
One small but critical regression can matter more than a strong global mean improvement.
Keep evaluation packs versioned
Release standards evolve. The platform should preserve which standard applied to which model and rollout.
Promote field failures into future evaluation coverage
Incidents and intervention-heavy situations should become replay and benchmark assets, not one-time anecdotes.
Bind evaluation to rollout
A candidate is not fully validated until the platform has early post-deploy evidence tied back to the release record.
Relationship To The Rest Of The Platform
- **Dataset Finalizer** creates immutable benchmark and holdout sets.
- **Experiment Tracker** preserves candidate comparison context.
- **Model Registry** stores release-ready candidates with evidence.
- **Deployment Manager** activates release policy in the field.
- **Telemetry** provides post-deploy validation and regression signals.
That connection is what makes release discipline durable instead of procedural theater.