This thesis will focus on how to apply Reinforcement Learning methods for solving deformable object manipulation tasks.
The Intelligent Robotics group will attend IROS 2021 with two accepted papers and a workshop mainly organised byPhD. Fares Abu-Dakka
Getting robots to autonomously learn to perform various tasks is often a long-term process, during which the robot’s exploratory actions can be unpredictable and potentially dangerous to the surrounding environment and to the robot itself. To mitigate the risk of hardware damage and to speed up the learning process, initial phases of learning are often […]
Programming robots to perform various tasks often requires extensive domain knowledge and a tedious programming process. The size of the program rapidly grows with task complexity; explicitly programming a robot to perform challenging tasks in a variety of environments would require the programmer to write routines for an enormous number of situations that may possibly […]
Robotic tasks in real-world applications generally involve uncertain, stochastic and dynamic environments. Pre-programming based solutions either do not work or give unsatisfactory results in such environments. This requires to generate cautious control strategies that provide optimum actions to perform the desired task while considering the effects of the uncertainties in the environment. Robot control aims […]