Learning Deep Policies For Physics Based Robotic Manipulation In Cluttered Real World Environments

Learning Deep Policies for Physics-based Robotic Manipulation in Cluttered Real-world Environments

Robotics in Extreme Environments

Topic editor Rustam Stolkin is director of A.R.M Robotics Ltd. All other topic editors declare no competing interests with regards to the Research Topic subject.

Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications

Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.