Robotics

MERL is looking for a self-motivated graduate student to work on Visual Simultaneous Localization and Mapping (V-SLAM). Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to): camera pose estimation, feature detection and matching, visual-LiDAR data fusion, pose-graph optimization, loop closure detection, and image-based camera relocalization. The ideal candidate would be a PhD student with a strong background in 3D computer vision and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.

Required Specific Experience

• Experience with 3D Computer Vision and Simultaneous Localization & Mapping.

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 looking for a highly motivated individual to work on whole-body dexterous manipulation. The research will develop robot motor skills for whole-body, dexterous manipulation using optimization and/or learning algorithms. The ideal candidate should have experience in either one or multiple of the following topics: Optimization Algorithms for contact systems, Reinforcement Learning, control through contacts, and Behavioral cloning. 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. Good coding skills in Python ML libraries like PyTorch etc. and/or relevant Optimization packages is required. A successful internship will result in submission of results to a peer-reviewed robotics journal in collaboration with MERL researchers. The expected duration of internship is 4-5 months with start date in May/June 2025. This internship is preferred to be onsite at MERL.

Required Specific Experience

• Prior experience working with physical hardware system is required.
• Prior publication experience in robotics venues like ICRA,RSS, CoRL.