Dynamical Systems

MERL is looking for a highly motivated and qualified PhD student in the areas of motion planning, machine learning and control for robotics, to participate in research on advanced algorithms for motion planning and skill learning of robotic systems. Solid background and hands-on experience with classical motion planning and trajectory optimization algorithms for robotic manipulators is expected. Exposure to machine learning for policy optimization and skill learning, understanding of various optimization solvers and control theory is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Solid experimental skill and hands-on experience in coding in Python and ROS are required for the position. A successful internship will result in submission of results to top tier robotics venue in collaboration with MERL researchers. Start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their updated CV and list of publications.

Required Specific Experience

• Experience with robotic manipulators or other system like robot quadrupeds is required.
• Experience with motion planning and trajectory optimization algorithms
• Strong programming skills in Python and ROS
• Experience in at least one physics simulator

MERL is seeking a highly motivated intern to research low-complexity (i.e., computationally efficient) formulations of model predictive control (MPC). Candidates should be currently enrolled in a PhD program and have theoretical background in MPC (e.g., an understanding of standard proofs of stability) and relevant concepts in convex optimization (e.g., an understanding of interior-point, active set, and first-order optimization methods). An ideal candidate would have prior research experience related to suboptimal MPC, real-time iterations strategies for MPC, and/or other low-complexity approximation methods for MPC, and convex optimization.

Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

Required Specific Experience

• Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
• Understanding of fundamental theoretical concepts in MPC (e.g., proofs of stability, recursive feasibility, etc.)
• Familiarity with optimization algorithms commonly used in MPC (e.g., interior-point, active set, and first-order methods)
• Strong programming skills in MATLAB, Python, and/or C/C++.

Additional Desired Experience

• Prior research experience related to suboptimal MPC and/or other low-complexity approximation methods for MPC.
• Prior research experience related to optimization algorithm development/analysis.

MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates are PhD students with experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, station keeping, rendezvous, landing, attitude control, orbit control, orbit determination, nonlinear estimation, scheduling problems, and optimization-based control. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

Required Specific Experience

• Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
• Strong programming skills in Matlab, Python, and/or C/C++