The mission of our lab, PeARL (Persistent Autonomy and Robot Learning Lab) is to develop novel robot learning algorithms that will allow robotic platforms to interact with humans effortlessly, learn new skills autonomously, and generalize their capabilities robustly in dynamic environments. To achieve these goals, we investigate how machine learning, AI, and novel sensors/hardware can be used to advance the state of the art of robot learning and to improve robots' level of autonomy. Our research is carried out on manipulators, mobile robots, and mobile manipulation platforms. We are looking for motivated researchers to help make this vision a reality!
You will work on novel robot learning algorithms that combine model-based and machine learning methods (both Imitation Learning and/or (Inverse) Reinforcement Learning) to accomplish effortless physical human-robot interaction or persistent autonomy in dynamic and harsh environments. The algorithms can be designed for fixed manipulator arms and/or mobile manipulator platforms, as well as walking robots.
The assistantship includes a tuition waiver and a graduate student stipend. UMass Lowell is a Carnegie Doctoral High Research (RU/H) university ranked in the top tier of US News' National Universities and is located 30 miles northwest of Boston in the northeast Massachusetts high-tech region. There are over 120 robotics companies in the local area. The successful candidate will have access to the PeARL lab (Persistent Autonomy and Robot Learning Lab) and several robotic platforms including manipulator, mobile, walking, and mobile manipulator robots.
The evaluation of the received applications starts immediately and will continue until the positions are filled. Interested students are strongly encouraged to apply early, as the hire of successful candidates will take place on the first-come-first-served basis. The desired start date is September 2019.
Please send, as a single PDF document:
The application files should be sent as a single PDF file to: email@example.com