The goal of this project is to exploit more comprehensive information from humans, in order to learn as many skill patterns from humans as possible according to the tasks at hand.
The goal of this assignment is to understand the KMP and to implement a C++ code for the kernelized treatment of orientation data in real setup.
The goal of this assignment is to understand dynamic movement primitives in the context of geometry awareness and provide a C++ code for the algorithm.
The goal of this thesis is to increase the efficiency of reinforcement learning when limited number of examples is available by providing a method of obtaining a large number task-specific trajectories from only a few demonstrations.
The goal of this thesis is to integrate KMP with reinforcement learning to provide an automatic adaptation approach to adapt the trajectory and goal in order to optimize a desired task.
In many robot control problems, skills such as stiffness, damping and manipulability ellipsoids are naturally represented as symmetric positive definite (SPD) matrices, orientations are represented as unit quaternions, sensory data processed as spatial covariances, etc., which capture the specific geometric characteristics of those skills. Typical learned skill models such as dynamic movement primitives (DMPs), probabilistic […]