The future Internet of Things (IoT) will have a deep economical, commercial and social
impact on our lives. The participating nodes in IoT networks are usually resource …

Robots such as drones, ground rovers, underwater vehicles and industrial robots have
increased in popularity in recent years. Many sectors have benefited from this by increasing …

We present foundations for using Model Predictive Control (MPC) as a differentiable policy
class for reinforcement learning. This provides one way of leveraging and combining the …

Eco-driving is a term used to refer to a strategy for operating vehicles so as to minimize
energy consumption. Without any hardware changes, eco-driving is an effective approach to …

Fuel cell electric vehicles use fuel cells as their main power source; the vehicle is driven by
an electric motor, and have an electric battery as a secondary power source that stores …

Abstract Domain-specific modeling priors and specialized components are becoming
increasingly important to the machine learning field. These components integrate …

Model-free Reinforcement Learning (RL) offers an attractive approach to learn control
policies for high dimensional systems, but its relatively poor sample complexity often …

For autonomous underwater vehicles (AUVs), control over AUV heading is of key
importance to enable high-performance locomotion control. In this study, the heading control …

We develop an approach to improve the learning capabilities of robotic systems by
combining learned predictive models with experience-based state-action policy map**s …

We present an algorithm for rapidly learning neural network policies for robotics systems.
The algorithm follows the model-based reinforcement learning paradigm and improves upon …