Author: Abu-Dakka Fares J.
Master thesis on “Efficient learning of task-specific trajectories from demonstrations”
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.
Master thesis on “Reinforcement learning with kernelized movement primitives”
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.
Exploiting Object Physical Properties for Grasping
In robotic manipulation, robots are required to interact with, and adapt to, unknown environments and objects. In order to successfully accomplish these tasks, robots need to identify various properties of the objects to be handled. For these reasons, identifying object models that can represent the properties of objects has become a crucial issue in robotics. […]
Video on “Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements”
Watch the video demonstration of our latest paper on friction coefficient estimation
Master thesis on “Hybrid learning and control for human-robot interaction: an imitation learning perspective”
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.
Assignment on “Orientation Learning and Adaptation in Cartesian Space”
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.
Assignment on “Geometry-aware Dynamic Movement Primitives”
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.