Hand posture recognition technology makes humancomputer interaction more natural and efficient. Existing hand posture recognition algorithms are mainly based on RGB images or depth data, each of which has its limitations: the former is susceptible to the interference of lighting and background color, while the latter is difficult to capture details and affects accuracy. To … [Read more...] about Dynamic Importance-Weighted Fusion Network Based on Dynamic Convolutions for Hand Posture Recognition: A Technique Based on Red, Green, Blue Plus Depth Cameras
Hands
Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework
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
