MERL is seeking a highly motivated intern to conduct original research in high precision pose estimation of deformable objects. Applicants are required to have a strong background in image processing, machine vision and point cloud processing using depth cameras. The internship is open to PhD students, preferably specializing in Computer Vision, with a strong publication record, solid programming skills in Python and/or C/C++, and preferably some experience using tactile sensors. Internship duration and start date are flexible.

MERL is seeking a highly motivated and qualified intern to conduct research into

hybrid modeling and control of object contact and collision for precise robotic assembly.

The ideal candidate is expected to be working toward a Ph.D. or equivalent degree

in the area of modeling and control of hybrid systems (those with both continuous and

discrete states), with strong knowledge and interest in differential algebraic

equations (DAEs), nonlinear and hybrid control theory, geometric algebra

and coordinate-free geometric methods. The research involves formalizing and extending

a hybrid DAE-based method of modeling the physics of object contact and collision to

include acausal effects of friction, address contact constraints of dimension

greater then one among objects, and formalize the method using hybrid systems theory

to study well-posedness issues and enable application of optimal control theory for

path planning and control of robotic assembly problems. 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 tailored computational algorithms for numerical optimal control of hybrid dynamical systems and applications for decision making, motion planning and control of autonomous systems. The research will involve the study and development of numerical optimal control methods for systems with continuous dynamics and discrete logic, nonsmooth and/or switched dynamics, and the implementation and validation of such algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: mixed-integer programming (MIP), mathematical programs with complementarity constraints (MPCCs), modeling and formulation of optimal control problems for hybrid dynamical systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on MIPs, MPCCs or numerical optimal control, are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.