Editorial: Advanced learning control in physical interaction tasks

Chao Zeng, Jing Guo, Qiang Li, Chenguang Yang

Abstract: Robotics are increasingly and urgently expected to acquire human-like dexterous manipulation skills in physical interaction environments. Due to this, the loop between the high-level action policy and the low-level motion execution needs to be closed by developing advanced data-driven or model-based learning and control approaches. Although recent studies have been shown to demonstrate promising results and advances in closed-loop learning control algorithms, several key issues remain quite a changeling.Typically, three problems in this field of research are yet to be solved:1) how to integrate learning and control models seamlessly for more dexterous manipulation and interaction performances; 2) how to compute learning and control policies from multi-modal/cross-modal data; and 3) how to make use of advances in both data-driven and model-based models for compliant and flexible interactions. We publish this researchtopic to bring together the newest theoretical findings and experimental results in advanced learning control applied to robot-environment physical interaction systems. Five manuscripts out of all submissions to this special collection are accepted after a standard review process. Below we give a brief review of the published articles.\cite{qin2022robot} summarize the state-of-the-art research on robotic tool usage, which is of great importance in physical interaction tasks. In this survey, they first give the definition of robot tool usage that is necessary t...