Editorial: Advanced learning control in physical interaction tasks

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 Research Topic 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 Research Topic 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 Research Topic are accepted after a standard review process. Below we give a brief review of the published articles.