Imitation learning

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios that require strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious … [Read more...] about Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework