Preference-driven demonstrations ranking for inverse reinforcement learning

New flexible teaching methods for robotics are needed to automate repetitive tasks that are currently still done by humans. For limited batch sizes, it is too expensive to teach a robot a new task (Smith & Anderson, 2014). Ideally, such flexible robots can be taught a new task by a non-expert. A non-expert is a person who knows the task the...

Reinforcement Learning of Visual Features

The digital environment has an ever increasing amount smart programs. Programs that also get smarter every day. They help us filtering spam e-mail and they adjust to show us personalized advertisements. These smart programs observe people and serve (other) people. A robot can be seen as a program with a body. Make the program smart enough and it...

Reinforcement Learning of Potential Fields to achieve Limit-Cycle Walking

Reinforcement learning is a powerful tool to derive controllers for systems where no models are available. Particularly policy search algorithms are suitable for complex systems, to keep learning time manageable and account for continuous state and action spaces. However, these algorithms demand more insight into the system to choose a suitable...

Reinforcement Learning for Tracking Control in Robotics

For a robot manipulator, an accurate reference tracking capability is one of the most important performance factor. This is especially the case for a robot which is deployed for an industrial use such as welding and laser cutting. Recently, an emerging application of 3D printing also attracts researchers to use robotic manipulators in hope of...

Speeding Up Reinforcement Learning with Graphics Processing Units

Conventionally programmed systems (e.g. robots) are not able to adapt to unforeseen changes in their task or environment. Reinforcement learning (RL), a machine learning approach, could grant this flexibility. Many fields of work could greatly benefit from this, be it in terms of cost, time or some other parameter. With RL, a learning agent...

Imitation learning for a robotic precision placement task

In industrial environments robots are used for various tasks. At this moment it is not feasible for companies to deploy robots for productions with a limited batch size or for products with large variations. The use of robots for such environments can become feasible through a new generation of robots and software which can adapt quickly to new...

Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control

This thesis investigates the applicability of the Probabilistic Inference for Learning COntrol (PILCO) algorithm to large systems and systems with time varying measurement noise. PILCO is a state-of-the-art model-learning Reinforcement Learning (RL) algorithm that uses a Gaussian Process (GP) model to average over uncertainties during learning....

Learning while preventing mechanical failure due to random motions

In this thesis one of the negative effects of learning from scratch on the durability of LEO is analysed. LEO is one of the bipedal walking robots of the TU Delft Robotics Institute. It uses Reinforcement learning to learn a stable and energy efficient walking gait. LEO’s learning algorithm causes its gears to fail faster during the initial...

Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords

The reinforcement learning (RL) framework enables to construct controllers that try to find find an optimal control strategy in an unknown environment by trial-and-error. After selecting a control action, the controller receives a numerical reward. The reward signal is based on the current state of the environment and the applied control action....

Avoiding failure states during reinforcement learning

The Delft Biorobotics Laboratory develops bipedal humanoid robots. One of these robots, called LEO, is designed to learn to walk using reinforcement learning. During learning, LEO will make mistakes and fall. These mistakes can cause serious dam- age to the system but are an integral part of the learning process. A likely solution is punishing...