AI Agent Guide
Knowledge Guides /How do you evaluate autonomous navigation agents, and why is one successful run not enough?
Last updated: · New Horizon Digital Outsourcing Team
Autonomous navigation evaluation should cover task completion, collisions, elapsed time, paths, and recovery behavior. Reaching the destination once demonstrates only that one scenario works. Evaluation must cover different start and end points, obstacle changes, and localization anomalies, while retaining failed results.
Fix the scenario and task definitions
- Define the map, robot dimensions, motion limits, start and end points, and timeout rules. Agree on arrival thresholds and collision criteria in advance to prevent them from being relaxed during evaluation. Dynamic-obstacle trajectories and random seeds should be reproducible so policy versions can be compared.
Distinguish planning issues from control issues
- Record path-planning failures, local obstacle-avoidance deadlocks, and tracking errors separately. Localization drift and missing sensor data should not all be attributed to poor navigation algorithms. Logs should expose states, paths, commands, and environmental changes to reviewers, rather than only a final score.
Check recovery and stopping behavior
- Create scenarios with blocked routes, unreachable targets, and brief localization anomalies to observe whether the agent stops, replans, or requests takeover correctly. Endless retries should not count as normal ongoing execution. Applications on real equipment also require an independent safety chain and cannot rely solely on agent judgment.
How should improvements between versions be reported?
- Compare old and new versions using the same evaluation set, reporting success rates, failure types, and resource costs. State explicitly if average completion time is calculated only from successful samples. Deliver configurations, scenarios, and failure replays so the reproducibility of improvements can be assessed.