MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems. The internship will also include experimental validation of the proposed algorithms in various robotic testbeds (quadrotors and mini-cars) at MERL. The ideal candidate is experienced in multi-agent motion planning and control, and has successfully demonstrated some of their prior work on hardware testbeds. The candidate must be proficient in ROS and C/C++, and at least familiar with Python and MATLAB. Prior experience with crazyflies and/or hamster robots will be considered a plus. The expected duration of the internship is 3-6 months, and the start date is Summer/Fall 2022. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is looking for a self-motivated and qualified candidate to work on modeling for contact phenomenon. Robotic manipulation is heavily affected by external contacts that can be modeled with physical and data driven models. We are interested in researching those models for analysis and control purposes. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Contact modeling and robotic manipulation. Physic Engines like Mujoco, Bullet, Drake and sim2real gap problems. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Experience in working with robotic systems. Knowledge in learning from demonstration algorithms and standard Reinforcement Learning algorithms is a plus. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms that will lead to a scientific publication. Typical internship length is 3-4 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.