Machine Learning

Machine learning is widely used to build models from data. Here at ACRL, machine learning is explored in the following research areas.

Distributed Reinforcement Learning Algorithm for Multi-UAV Applications

The problem of learning a global map using local observations by multiple agents lies at the core of many control and robotic applications. Once this global map is available, autonomous agents can make optimal decisions accordingly. In this project, we study a distributed RL algorithm for multi-agent UAV applications. In the distributed RL, each agent makes state observations through local processing. The agents are able to communicate over a sparse randomly changing communication network and can collaborate to learn the optimal global value function corresponding to the aggregated local rewards without centralized coordination. More >>

Elderly Care

This project aims to explore the use of small aerial and ground co-robots in domestic environment to assist older population in their daily activities. Study shows that for the types of daily activity assistance, fetching objects from the floor or another room, reaching for objects, and finding/delivering items are among those tasks which are preferred to be completed by robot. To that end, various techniques including machine learning, mechanical design, system analysis, and control design have been explored and integrated in this research program. More >>

Socially Trustable Drones

The goal of this project is to provide the foundations to address human related concerns that arise in multiple human-robot systems, where robots have to perform tasks in the presence of (and in cooperation with) humans. In particular, we are targeting the fundamental understanding of two issues that are crucial in the integration of robotic systems into real-life human populated environments: first, how humans perceive autonomous mobile robots as a function of robots’ appearance and behavior; second, how to design and control mobile robots to improve the level of comfort and perceived safety of the people present in the environment. More >>