A multi-terrain locomotion policy for quadruped robots, trained in Isaac Lab and tested in GPS-denied indoor environments built to simulate post-disaster scenarios.
When a building collapses, the first 72 hours are when most survivors are found. Sending humans into unstable debris is dangerous. Wheeled robots get stuck. Drones can't carry sensors close to the ground. Legged robots can go where nothing else can.
After earthquakes, explosions, or structural failures, the operating conditions are unlike anything a standard robot is designed for. Floors are uneven. Corridors are blocked. There is no GPS. Lighting is unpredictable. And every second of delay matters.
Wheeled platforms lose traction on rubble and cannot climb over obstacles more than a few centimetres tall. Aerial robots struggle with endurance and cannot get close enough to debris piles to be useful. Legged robots are different. They can step over obstacles, maintain balance on uneven ground, navigate narrow passages, and carry sensor payloads deep into environments no human should enter.
The challenge is making them autonomous enough to do this reliably without constant operator input. That is what this project worked toward.
At SpaceData Inc. I was tasked with extending a flat terrain locomotion policy to handle the kinds of surfaces a robot would actually encounter in a disaster zone. That meant training a reinforcement learning policy in Isaac Lab that could maintain 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 step is validating it on physical hardware, tightening the navigation integration for tighter corridors, and improving 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 an actual operational constraint — not a research demo, but a system someone would actually deploy in the field. That changes how you think about every design decision.