Edge-Case Data Collection for Robotics

Edge-case data collection is the deliberate capture of rare failures, near-misses, recoveries, and long-tail behaviors so evaluation reflects real operational risk rather than clean demonstrations. Operant co-defines a failure taxonomy and sampling targets with your team, then runs scripted and opportunistic capture under safety review. The result is tail-event libraries that make your evaluation honest about how a policy behaves when things go wrong.

Why tail events matter

A policy that succeeds on 95% of happy-path episodes can still be unsafe, because deployment risk concentrates in the rare 5%: slips, collisions, dropped objects, planner stalls, and the recoveries that follow. Evaluation that ignores the tail overstates reliability. Capturing tail events deliberately is the only way to measure it.

Failure taxonomy

We start by co-defining a taxonomy of the failures that matter for your system, then map each to capture targets. This shared vocabulary keeps collection, labeling, and evaluation aligned across the program.

Sampling ratios

Because failures are rare, we set explicit sampling targets and combine scripted scenarios with opportunistic capture to hit them. This pairs naturally with sim-to-real data collection, since many tail events are exactly what simulation fails to reproduce.

Safety protocols

Capturing failures safely requires protocols agreed before any capture begins, review of risky scenarios, controlled environments, and clear stop conditions. Safety is scoped alongside the taxonomy.

Delivery and eval usage

Tail events are labeled and organized into evaluation slices, delivered with metadata and provenance. See warehouse defective-SKU pick failures for a concrete failure-capture scenario, and eval benchmarks vs. the real world for how tail data sharpens evaluation. Edge-case capture is a core part of any serious robotics data collection program.