Locomotion & navigation
A machine that moves through the world has to answer a question at every step: can I put weight here, and what happens next if I do. Terrain is not uniform, and the interesting cases are the ones that appear rarely.
Legged locomotion is the subsystem where learned control has most clearly beaten hand-written control, and it got there through simulation at enormous scale. But simulation only contains what someone thought to model. The failures that matter — a loose kerb edge, wet tile, gravel that gives way — are discovered in the real world, labelled by people, and fed back. The long tail is not an inconvenience here; it is the entire safety case.
What this needs from people
- Traversability judged surface by surface — gravel, wet tile, a loose kerb edge.
- Continuous-frame geometry, so the map stays coherent while the sensor is moving.
- Free space and boundaries marked with the precision a manoeuvre actually needs.
- The rare event kept, not smoothed away, because that is the case that causes the accident.
The research
Where this subsystem actually stands.
Talks & demonstrations
The people building it, in their own words.
How robots learn to hike
Marco Hutter — Learning Control for Legged Robots
Where HSV fits
Our 3D LiDAR line already produces continuous-frame labelled point clouds for autonomous perception and mapping, and our parking-slot work is navigation geometry at close tolerance — slot boundaries and inner corner points that a vehicle has to trust to move safely in a confined space.
See the delivery lineThe rest of the figure