Researchers at ETH Zurich have developed an innovative autonomous robotic hand capable of walking on its fingers while simultaneously performing manipulation tasks. Unlike conventional robotic systems that require separate mechanisms for mobility and object interaction, this approach transforms a commercially available five-fingered robotic hand into a self-contained mobile robot. Using AI and reinforcement learning, the researchers trained the hand in simulation to navigate different surfaces, maintain balance, recover from falls, press keyboard keys, and push objects. Equipped with a compact onboard computer, battery, and motion sensors, the robotic hand successfully traversed 14 different surfaces, achieved an average walking speed of 9 centimetres per second, and demonstrated autonomous object manipulation. Its learning framework addresses the challenges posed by the hand's asymmetrical structure, enabling coordinated finger movements for both locomotion and manipulation.

The research introduces a promising direction for autonomous modular robotics, where individual robotic components can dynamically switch between different functions rather than remaining permanently assigned to specific tasks. Potential applications include industrial maintenance, inspection of confined environments, search-and-rescue operations, and advanced robotic manipulation in spaces inaccessible to conventional robotic arms. By combining mobility, dexterity, and intelligent control within a single compact platform, the technology could contribute to a new generation of adaptable, multifunctional robotic systems. Nevertheless, challenges remain regarding autonomous perception, navigation, mechanical durability, and reliability in complex environments. The broader vision is to develop robotic architectures in which individual components can operate independently or collaboratively, improving flexibility and adaptability across real-world applications.
More information:
https://spectrum.ieee.org/walking-robotic-hand
