About this Research Topic
In this Research Topic, we will focus on an open, challenging and hot Research Topic: robotic dexterous grasping and manipulation, where a robot is required to understand, as well as adapt to, complex scenarios and grasp different known or unknown objects. Due to the influence of miscellaneous lighting conditions and the intricate spatial positions of objects, most objects, especially whose 3D shapes are relatively sophisticated, will probably have an insurmountable gap between the detected appearance and the real situation. Classical grasping methods include generating the analytic model, which need large amount of prior knowledge or modeling parameters of objects with poor generalization. In recent years, new approaches combining with artificial intelligence, such as learning-based methods, provide new directions for solving this problem. For example, vision-based machine learning (including object localization, object pose estimation and grasp estimation, etc.) is a promising approach to accomplish dexterous robot grasping and manipulation in complex scenarios, with the ability to generalize to handle new objects. Other directions include the design of new mechanisms and end effectors for grasping.
Topics of interest include, but are not limited to, the following:
• Planning and control for robotic dexterous grasping
• Robotic perception in dexterous grasping and manipulation
• Robotic mechanisms of robotic end effectors and hand
• Multi-sensor systems for dexterous grasping and manipulation
• Humanoid dexterous hand design for grasping
• Learning and adaptation for dexterous grasping in cluttered scenes
• Datasets and virtual environments for dexterous grasping and manipulation
• Continuous learning for dexterous grasping and manipulation
• Applications and systems of dexterous grasping and manipulation
• Interdisciplinary research (such as neural mechanisms about dexterous manipulation)
Keywords: Robotic manipulation, Dexterous Grasping, Robotic End Effectors, Robotic perception, Robot learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.