Robots learn to recover from dangerous stair falls using a smart stabilising system

Researchers at SUTD’s ROAR Laboratory built a reinforcement-learning fall-mitigation system for stair-climbing service robots.

Using a three-jointed rear-mounted arm, the robot learned to recover from five common stair-fall modes in simulation.

The system achieved a 69.4% average recovery success rate, outperforming a hand-coded controller.

It generalized across modest changes in robot size and stair layouts, but still falls short of industrial safety standards like IEC 61508.