TargetNode's AI anomaly detector learns robot norms, no tuning needed
Ex-robotics engineer’s system flags faults in real time without manual thresholds.
Shane Hill, a former engineer at a robotic dishwasher startup (Armstrong.ai), grew tired of manually setting and tuning thresholds for robot diagnostics. After his company closed, he built an automated anomaly detection system that requires no hardcoded rules or threshold adjustments. The system watches a robot’s topics in real time, learns its normal operating pattern, and flags deviations instantly. It has been validated on five public datasets covering diverse platforms: a UR3e arm (CASPER), a Parrot Bebop 2 and a 3DR Solo with real propeller damage (PADRE), a CarbonZ T-28 fixed-wing aircraft (ALFA), and a large set of PX4 flights with state-estimation anomalies (UAV-SEAD).
Hill is also running the system on his own production servers (29 signals) and reports zero false alerts over 200 hours. Now he’s looking for beta testers to run the system on real robots in production. Participants get free anomaly detection, direct influence on product development, and help from Hill to get it installed. In return, he wants honest, detailed feedback on what’s missing, confusing, or broken. Interested parties can reply to the ROS General thread or email shane@targetnode.ai.
- Zero manual threshold tuning – the system learns a robot's normal behavior autonomously.
- Validated on 5 public datasets (CASPER, PADRE, ALFA, UAV-SEAD) plus 200 server-hours with no false alarms.
- Beta testers get free real-time anomaly detection for their robots in exchange for feedback.
Why It Matters
Automated anomaly detection reduces maintenance overhead and downtime for robotic fleets without constant human tuning.