ETH Zurich researchers trained a robotic hand to use its five fingers as legs for untethered locomotion, with onboard power, sensing, and computation.
Reinforcement-learning policies enable the system to crawl, steer, recover from falls, and adapt its locomotion across 14 indoor and outdoor surfaces, including carpet, tile, asphalt, grass, and gravel.
The hand can also perform keyboard presses while supporting its own weight and use overhead visual feedback to push objects toward target locations.