Researchers at ETH Zurich's Soft Robotics Lab trained a robotic hand to crawl on its fingers, recover after falls and manipulate objects while supporting its own weight. The 818-gram platform combines an off-the-shelf, five-fingered hand with onboard power and computing. Policies trained through reinforcement learning in simulation enabled untethered crawling across 14 indoor and outdoor surfaces.
Separate policies control different skills rather than one general-purpose controller. Hardware trials achieved 21 successful recoveries in 25 attempts and 29 correct keyboard commands in 32 attempts. Keyboard demonstrations began with manual alignment and followed operator-issued commands; pushing a cube towards a target required overhead visual feedback. The work is presented as a research preprint.