Robotics

MERL is seeking interns passionate about robotics to contribute to the development of an Active Simultaneous Localization and Mapping (Active SLAM) framework guided by Large Language Models (LLM). The core objective is to achieve autonomous behavior for mobile robots. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms, such as legged and wheeled robots. The expectation at the end of the internship is a publication at a top-tier robotic or computer vision conference and/or journal.

The internship has a flexible start date (Spring/Summer 2025), with a duration of 3-6 months depending on agreed scope and intermediate progress.

Required Specific Experience

• Current/Past Enrollment in a PhD Program in Computer Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, or related field
• Experience with employing and fine-tuning LLM and/or Visual Language Models (VLM) for high-level context-aware planning and navigation
• 2+ years experience with 3D computer vision (e.g., point cloud, voxels, camera pose estimation) and mapping, filter-based methods (e.g., EKF), and in at least some of: motion planning algorithms, factor graphs, control, and optimization
• Excellent programming skills in Python and/or C/C++, with prior knowledge in ROS2 and high-fidelity simulators such as Gazebo, Isaac Lab, and/or Mujoco

Additional Desired Experience

• Prior experience with implementation and/or development of SLAM algorithms on robotic hardware, including acquisition, processing, and fusion of multimodal sensor data such as proprioceptive and exteroceptive sensors

MERL is seeking an outstanding intern to collaborate in the development of motion planning and control for autonomous articulated vehicles. The ideal candidate is expected to be working towards a PhD in electrical, mechanical, aerospace engineering, robotics, control or related areas, with a strong emphasis on motion planning and control, possibly with applications to ground vehicles, to have experience in at least some of path/motion planning algorithms (A*, D*, graph-search) and optimization-based control (e.g., model predictive control), to have excellent coding skills in MATLAB/Simulink and a strong publication record. The expected start date is the Spring/Early Summer 2025 and the expected duration is 6-9 months, depending on candidate availability and interests.

Required Specific Experience

• Path/motion planning algorithms (A*, D*, graph-search)
• Nonlinear model predictive control
• Programming in Matlab/Simulink
• Applications to mobile robots or vehicles

Additional Useful Experience

• Nonlinear MPC Design in CasADi
• Code generation tools and dSPACE
• Applications to autonomous vehicles and articulated vehicles

MERL is seeking a graduate student to develop a scalable optimization-based framework for feedforward-feedback co-design for nonlinear dynamical systems subject to path constraints. The framework will 1) support modeling and operational uncertainties, and 2) guarantee static and dynamic feasibility in closed-loop. The solution approach will leverage the state-of-the-art in sequential convex programming, contraction analysis, and first-order methods for semidefinite programming. The methods will be evaluated on high-dimensional motion planning problems in robotics. The results of the internship are expected to be published in top-tier conferences and/or journal in robotics, control systems, and optimization.

The internship is expected to start in Spring or Summer 2025 with an expected duration of 3-6 months depending on the agreed scope and intermediate progress.

Required Specific Experience

• Current/Past enrollment in a Ph.D. program in Mechanical, Aerospace, Electrical Engineering, Computer Science, or Applied Mathematics.
• 2+ years of research in at least some of: first-order algorithms for SDPs, contraction analysis, nonconvex trajectory optimization.
• Strong programming skills in Python and/or C/C++.