I trained a multi terrain locomotion policy for quadruped robots in Isaac Lab and tested it in indoor environments without GPS, built to simulate post disaster scenarios.
When a building collapses, most survivors are found in the first 72 hours. Sending humans into unstable debris is dangerous, wheeled robots get stuck, and drones cannot carry sensors close to the ground. Legged robots can go where nothing else can.
After earthquakes, explosions, or structural failures, the operating conditions are not like anything a standard robot is designed for. Floors are uneven, corridors are blocked, there is no GPS, and lighting is unpredictable. Every second of delay matters.
Wheeled platforms lose traction on rubble and cannot climb over obstacles more than a few centimetres tall. Aerial robots have short endurance and cannot get close enough to debris piles to be useful. Legged robots are different. They can step over obstacles, keep balance on uneven ground, move through narrow passages, and carry sensor payloads deep into environments no human should enter.
The hard part is making them autonomous enough to do this reliably without constant operator input. That is what this project worked toward.
At SpaceData Inc. my job was to extend a flat terrain locomotion policy so it could handle the kinds of surfaces a robot would really meet in a disaster zone. To do this I trained a reinforcement learning policy in Isaac Lab that keeps stable locomotion across rubble, slopes, steps, and irregular ground without pre mapping the terrain.
The multi terrain policy reaches its navigation goals reliably across the simulation environments I built. The next steps are to validate it on physical hardware, tighten the navigation integration for narrow corridors, and improve recovery behaviors after unexpected falls.
Most of the detailed results from this internship are under NDA. What I can say is that this was the first time I had to build something with a real operational constraint. It was not a research demo. It was a system someone would actually deploy in the field, and that changes how you think about every design decision.