Internship Openings

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 a highly motivated intern to collaborate in the development of outdoor, offroad applications of quadruped robots, with wildlife monitoring and farming as examples. The overall project involves multiple developments including robust gait control, optimal gait generation in uncertain terrain conditions, planning and allocation of multiple robots. The work will be validated in simulation first, and experimental validation will be possible (if time permits) on robotic platforms on-site. The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Fall/Winter 2025 (exact dates are flexible) with an expected duration of 3-6 months.

Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.

Required Experience

• Current enrollment in a PhD program in Mechanical, Electrical, Aerospace Engineering, Computer Science or related programs, with a focus on Robotics and/or Control Systems
• Experience in some/all of these topics:
  • Planning and control for legged robots
  • Modeling and control in offroad scenarios
  • ROS and simulation environment for robots control,
  • Strong programming skills in Python and/or C/C++

Additional Useful Experience

• Modeling of terrain uncertaint
• Robust control and planning under uncertainty
• Coverage control in uncertain scenarios
• Experience with computer vision

MERL is seeking a self-motivated and qualified individual to work on developing an integer/mixed-integer programming solver customarily designed for coordination planning of aerial drones. The ideal candidate will be a PhD student in computer science, mathematics, industrial engineering, or a related discipline, with a solid background in integer optimization. Preferred skills include knowledge of branch-price-and-cut algorithm or column generation, and hands-on experience with callbacks of the Gurobi Optimizer; strong programming skills and experience with at least one of Python, Julia, C/C++, Matlab are also expected. Publication of results produced during the internship is desired. The expected start date is in Fall 2025 or Spring 2026, for a duration of 3- months.

Required Specific Experience

• Significant hands-on experience with integer optimization.
  • Experience with trajectory optimization is a plus.
• Fluency in at least one of: Python, Julia, C/C++, Matlab
• Completed their MS, or >30% of their PhD program

MERL is seeking a highly motivated intern to collaborate in the development decision making, planning and control for teams of ground robot in task such as coverage control, monitoring and pursuit-evasion. The ideal candidate is a PhD student with strong experience in planning and control of multi-agent systems, with background in advanced model-based (e.g., MPC) and learning-based (e.g., RL) methods. The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Fall/Winter 2025 (exact dates are flexible) with an expected duration of 3-6 months.

Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.

Required Experience

• Current enrollment in a PhD program in Mechanical, Electrical, Aerospace Engineering, Computer Science or related programs, with a focus on Robotics and/or Control Systems
• Experience in as many as possible of:
  • Formal methods and set based methods (temporal logics, reachability, invariance)
  • Model predictive control (design, analysis, solvers)
  • Reinforcement learning for planning
  • Cooperative planning and control for multi-agent systems
  • Programming in Python or Matlab or Julia

Additional Useful Experience

• Knowledge of one or more physics simulators for robotics (e.g., MuJoco)
• Experience with coverage control and pursuit-evasion problems
• Programming in C/C++ or Simulink code generation

MERL is seeking a highly motivated and qualified intern to conduct research on applying foundation models to robotic manipulation. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to enable generalist manipulation capabilities across diverse objects, tasks and embodiments including humanoids. Potential research topics include few-shot policy learning, multimodal grounding of multiple sensor modalities to robot actions, and adapting foundation models for precise control and high success rate.

The ideal candidate will be a senior Ph.D. student with a strong background in machine learning for robotics, particularly in areas such as foundation models, imitation learning, reinforcement learning, and multimodal perception. Knowledge on large-scale Vision-Language-Action (VLA) and multimodal foundation models is expected. The internship will involve algorithm design, model fine-tuning, simulation experiments, and deployment on physical robot platforms equipped with cameras, tactile sensors, and force/torque sensors. The successful candidate will collaborate closely with MERL researchers, with the expectation of publishing in top-tier robotics or AI conferences/journals. Interested candidates should apply with an updated CV and relevant publications.

Required Specific Experience

• Strong background in machine learning for robotics, particularly foundation models (e.g., pi_0, OpenVLA, RT-X, etc.) and imitation learning.

• Experience with simulation environments such as Mujoco, Isaac Gym, or RLBench.

• Experience with physical robot platforms and sensors (vision, tactile, force/torque).

• Proficiency in Python, PyTorch, and modern deep learning frameworks

• Strong publication record in robotics, machine learning, or AI venues

Internship Details

• Duration: ~3 months
• Start Date: Fall 2025 (flexible based on mutual agreement)
• Goal: Publish research at leading robotics/AI conferences and journals

MERL is looking for a highly motivated and qualified PhD student in the areas of machine learning and robotics, to participate in research on advanced algorithms for learning control of robots and other mechanisms. Solid background and hands-on experience with various machine learning algorithms, including deep learning, is expected, as well as good understanding of computer vision methods, in particular algorithms for keypoint detection and tracking. Exposure to deep reinforcement learning and/or learning from demonstration 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. Familiarity with visual servocontrol is highly desirable. Solid experimental skills and hands-on experience in coding in Python, PyTorch, and OpenCV are required for the position. Some experience with ROS2 and familiarity with classical mechanics and computational physics engines would be helpful, but is not required. Hands-on familiarity with industrial robots will be a definite plus. The position will provide opportunities for exploring fundamental problems in incremental learning in humans and machines, leading to publishable results. The duration of the internship is 3 to 5 months. Preference will be given to candidates who can start no later than the beginning of January 2025.

Required Specific Experience

• Python, PyTorch, OpenCV
• Keypoint tracking in images

Desired Specific Experience

• Visual servocontrol of robots
• Learning diffusion policies
• MuJoCo or other physics engines
• System identification
• Clustering algorithms
• ROS2

MERL is looking for a highly motivated and qualified intern to work on methods for task-oriented 6-dof grasp pose detection using vision and tactile sensing. The objective is to enable a robot to identify multiple 6-DoF grasp poses tailored to specific tasks, allowing it to effectively grasp and manipulate objects. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for robotic grasping, object tracking, and imitation learning. This role involves developing, fine-tuning and deploying models on hardware. The successful candidate will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, and publish research findings at a top-tier conference. Start date and expected duration of the internship is flexible. Interested candidates are encouraged to apply with their updated CV and list of relevant publications.

Required Specific Experience

• Prior experience in robotic grasping
• Experience in Machine Learning
• Excellent programing skills