Cane-supported walking by humanoid robot and falling-factor-based optimal cane usage selection
We propose a scheme that enables humanoid robots to perform cane-supported walking. Cyclic, leg-like, and preventive cane usages are designed to adapt falling factors. The robot selects the optimal cane usage according to estimated falling factor. Affinities between cane usages and falling factors are used for the selection. Q-learning makes the robot learn the affinities via dynamics simulations.
humanoid robots cane usage q-learning selection algorithm for locomotion