Internship Openings

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 individual to work on learning algorithms for generalist robotic manipulation agent. In this internship we will be working on complex, assembly tasks. The research will develop novel learning-based algorithms for assembly tasks such as tight-tolerance insertion. The ideal candidate should have experience in either one or multiple of the following topics: Deep Reinforcement learning, Diffusion Models/Policies and Force Control for Manipulation. Senior PhD students in machine learning and engineering with a focus on Reinforcement/Imitation Learning and Manipulation are encouraged to apply. Prior experience working with physics engines like Mujoco, Isaac Gym, etc. is required. Prior experience working with physical robots as well vision and tactile sensors is required. The developed algorithm needs to be implemented on the real system. 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 RL environments (e.g., RL Bench) is required. The expected duration of internship is 3-4 months with expected start date in Sept 2025. This internship is preferred to be onsite at MERL.

Required Specific Experience

• Experience working with physical robot and sensors like cameras, tactile and F/T is a must.
• Prior experience with learning algorithms is a must.

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

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 and develop a framework for spatio-temporal monitoring using heterogeneous mobile robots. The work will involve multi-domain research, including multi-agent planning and control, optimization, adaptive and learning-based control, and computer vision. The methods will be implemented and evaluated using physical experiments on robotic platforms (e.g., Crazyflies,Turtlebots). 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 4-6 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 Specific Experience

• Current enrollment in a PhD program in Mechanical, Electrical Engineering, Computer Science, or related programs, with a focus on Robotics and/or Control Systems
• Experience in some/all of these topics: multi-agent planning and control, optimization, adaptive and learning-based control, and computer vision
• Experience with ROS2 and validation of algorithms on robotic platforms
• Strong programming skills in Python and/or C/C++

Desired Specific Experience

• Experience with Crazyflie quadrotors and the Crazyswarm2 library
• Experience with cvxpy and/or gurobipy
• Experience in convex optimization and model predictive control
• 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 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

Those who are passionate about pushing the boundaries of embodied AI, join our cutting-edge research team as an intern and contribute to the development of generalist AI agents for humanoid robots. This is a unique opportunity to work on impactful projects aimed at publishing in top-tier AI and robotics venues.

What We’re Looking For

We’re seeking highly motivated individuals with:

• Advanced research experience in robotic AI, edge AI, and agentic AI systems.
• Hands-on expertise in Large Language Models (LLMs), Vision-Language-Action (VLA) models and Foundation Models
• Strong proficiency with Python, PyTorch, deep learning, and robotic agent frameworks

Internship Details

• Duration: ~3 months
• Start Date: Flexible
• Goal: Publish research at leading AI/robotics conferences and journals

If you're excited about shaping the future of humanoid robotics and AI agents, we’d love to hear from you!