self-improving-roboticsCaptures robotics autonomy failures, operational incidents, and engineering learnings to enable continuous improvement across perception, localization, planning, control, simulation, safety, and hardware integration. Use when: (1) Robot fails to localize in dynamic environment, (2) Planner fails in narrow passage or obstacle-rich scene, (3) Oscillatory control behavior or unstable PID tuning appears, (4) Sensor desync occurs (camera-lidar-imu timestamp mismatch), (5) Hardware driver drops packets or CAN timeout occurs, (6) Safety stop or emergency brake triggers unexpectedly, (7) Simulation succeeds but real robot fails, (8) Thermal throttling, battery sag, or power brownout appears.
Install via ClawdBot CLI:
clawdbot install jose-compu/self-improving-roboticsGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/jose-compu/self-improving-robotics.gitAudited Apr 18, 2026 · audit v1.0
Generated May 11, 2026
An autonomous vehicle loses GPS and lidar-based localization in a dense urban area with tall buildings. The skill logs the incident, captures lidar scan matching errors, and promotes the learning to a calibration playbook for multi-sensor fusion timing. This reduces future localization drift events.
A warehouse robot repeatedly fails to plan a path through narrow aisles with obstacles, causing operational delays. The skill documents the planner's limitations, adds metadata for scene reproducibility, and eventually promotes a tuning runbook for navigation parameters. This improves warehouse throughput.
A robotic arm in an assembly line exhibits oscillatory behavior due to unstable PID tuning, leading to part misalignment. The skill logs control loop telemetry, identifies the root cause, and updates the tuning runbook to prevent recurrence. This increases manufacturing yield and reduces downtime.
An agriculture drone experiences camera-lidar-imu timestamp mismatch, causing inaccurate crop mapping. The skill logs the sensor fusion error and creates a calibration playbook for time synchronization procedures. This improves mapping accuracy and crop monitoring reliability.
A surgical robot performs perfectly in simulation but fails on real tissue due to unmodeled friction and compliance. The skill captures the sim-to-real gap, logs learnings under 'sim_to_real_gap', and suggests updates to simulation parameters. This accelerates surgeon training and reduces procedure risks.
Offer the skill as a cloud-based subscription that integrates with robot fleets to automatically log incidents and generate continuous improvement recommendations. Revenue comes from monthly per-robot fees plus premium consulting for safety checklist and runbook creation.
License the skill as an embedded tool within robotics operating systems or SDKs provided by OEMs to their enterprise customers. Revenue is based on per-unit royalties or annual licensing agreements with robotics companies.
Create a marketplace where robotics companies can buy, sell, and share curated learnings, safety checklists, calibration playbooks, and tuning runbooks. Revenue comes from transaction fees, premium listings, and subscription access to the database.
💬 Integration Tip
Start by creating the .learnings directory and log files as described in the skill's first-use initialization. Then integrate with your existing incident reporting workflow by pointing the skill to your current logging system or having it watch for common failure patterns automatically.
Scored Jul 13, 2026
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