Abstract: In this work, we investigate how a state-of-the-art grasp planner based on deep reinforcement learning performs when applied to a soft–rigid gripper in a decluttering task. The gripper, called Soft ScoopGripper (SSG), is endowed with a rigid scoop-shaped part that facilitates the interaction with the environment and with objects. We hypothesize that the clever … [Read more...] about Leveraging Embodied Mechanical Intelligence for Learning Decluttering Tasks: Gripper Design Boosts Learning
Hands
The Developments and Challenges Toward Dexterous and Embodied Robotic Manipulation: A Survey
Abstract: OKAchieving humanlike dexterous robotic manipulation remains a central goal and a pivotal challenge in robotics. The development of artificial intelligence (AI) has allowed rapid progress in robotic manipulation. This article summarizes the evolution of robotic manipulation from mechanical programming to embodied intelligence, alongside the transition from simple … [Read more...] about The Developments and Challenges Toward Dexterous and Embodied Robotic Manipulation: A Survey
Dynamic Importance-Weighted Fusion Network Based on Dynamic Convolutions for Hand Posture Recognition: A Technique Based on Red, Green, Blue Plus Depth Cameras
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
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
