Data collection is a major obstacle to applying deep learning methods to robotics. In this thesis, we propose to perform a quantitative study of the use of synthetic data from 3D software and physics simulators to train machine learning models that can later be deployed on physical systems.
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 […]