MERL is looking for a highly motivated and qualified intern to work on 6D pose estimation and tracking of multiple objects for robotic manipulation. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for 6D pose estimation, tracking of rigid objects and vision based scene understanding. The candidate should have a strong background in deep learning using unsupervised and self-supervised approaches and geometric scene understanding. The internship requires developing and implementing algorithms to estimate accurate 6D poses of multiple objects in a scene captured by an RGB-D camera with unknown positions. The method will be applied for robotic manipulation where the knowledge of accurate position and orientation of objects within the scene would allow the robot to interact with the objects. Experience working with a physics engine simulator like PyBullet, Issac Gym, or Mujoco is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researcher, and publication of the relevant results is expected. Start date in the summer or early fall is preferable, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

MERL is looking for a highly motivated individual to work on robust control of robotic manipulation system using vision and tactile feedback. The research will develop complex system using optimization, contact sensing and control techniques that can be used for closed-loop control of manipulation systems. The ideal candidate should have experience in either one or multiple of the following topics: optimization for contact-rich systems, manipulation, grasping and tactile sensing. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision and tactile sensors) is required as results need to be implemented on a physical hardware. A successful internship will result in submission of results to peer-reviewed conference and journals. Good coding skills in Python and state-of-the-art optimization packages like IPOPT, SNOPT, etc. is required. The expected duration of internship is 3-4 months with start date in Aug/Sept 2023. This internship is preferred to be onsite at MERL.