Internship Openings

MERL is seeking a highly motivated PhD student to conduct research on bimanual visuotactile manipulation for industrial applications, such as assembly, disassembly, and tool-enabled operations. The focus of this internship is on developing closed-loop manipulation skills for contact-rich tasks using visuotactile sensing and multi-modal learning from large foundation models. In particular, we are interested in exploring how VLA/RT-style vision-language-action models or foundation manipulation models can be adapted or fine-tuned for real-world bimanual settings. A successful internship will lead to a submission to a top-tier robotics venue (e.g., RSS, ICRA, CoRL) in collaboration with MERL researchers.

The internship duration is approximately 3 months, beginning in Summer or Fall 2026, and will be fully onsite at MERL.

Required Qualifications

• Senior PhD student in robotics, mechanical/electrical engineering, computer science, or related fields.
• Machine learning for robotic manipulation (e.g., RL, imitation learning, representation learning, VLA/foundation model fine-tuning), with an emphasis on real-world systems.
• Experience with visuotactile sensors (e.g., GelSight, GelSlim, DIGIT).
• Experience working with physical robotic hardware.
• Prior publication in top robotics venues (RSS, CoRL, ICRA).
• Strong programming experience in Python and/or C++, especially with ML or control frameworks (e.g., PyTorch, MuJoCo).

Preferred Qualifications

• Experience with foundation models for robotics, such as
  • Vision-Language-Action models (e.g., RT-2, RT-X, OpenVLA),
  • Multi-modal diffusion or transformer policies,
  • Fine-tuning pipelines (e.g., LoRA, RLF).
• Experience building high-quality robotics codebases (OOP, CI, unittests).

The pay range for this internship position will be 6-8K per month.

MERL is seeking a highly motivated and qualified intern to contribute to the next generation of adaptive human-robot interaction (HRI) technologies. This internship will focus on the development and validation of intelligent and collaborative robotic systems that seamlessly integrate with human partners, providing an opportunity to engage in cutting-edge research at the intersection of robotics, artificial intelligence, and human-centered automation. There are several research topics of interest, including robot learning from demonstrations, assistive teleoperation, shared autonomy, object handovers, and intent recognition. Applicants should be current Ph.D. students with strong expertise in robot learning and computer vision. The selected intern will collaborate closely with MERL researchers to design and implement novel algorithms, conduct experiments, and disseminate research findings through a top-tier conference. Start dates and internship durations are flexible. Interested candidates are encouraged to apply with an updated CV and a list of relevant publications.

Required Specific Experience

• Demonstrated experience in robot learning and computer vision for robotic applications, including diffusion policies, flow-matching methods, or Vision-Language-Action (VLA) models.

The pay range for this internship position will be 6-8K per month.

MERL is seeking a highly motivated and qualified intern to work on the problem of loco-manipulation using legged robots. The objective of the project is to develop specific algorithms for simultaneous quadrupedal locomotion and prehensile manipulation of 3D objects placed outside the immediate workspace of the mobile robot. The ideal candidate would be a graduate student on a Ph.D. track with advanced knowledge of legged locomotion, grasp pose detection, machine vision and contact-aware object manipulation. The software developed within this project should be validated and fine-tuned on a physical legged robot during the internship. The successful candidate will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, publish research findings at a top-tier conference and assist with filing a potential associated patent. 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

• Experience with Isaac Lab, MuJoCo or other physics simulators
• Proficient with at least one programming language, preferably C++ or Python
• Experience in Machine Learning
• Prior experience with physical quadrupedal or bipedal robots

The pay range for this internship position will be 6-8K per month.

MERL is seeking a highly motivated and qualified intern to conduct research on applying foundation models to manufacturing scenarios. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to specialize generalist manipulation policy to obtain high success rate in diverse but specific tasks. 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: Summer 2026 (flexible based on mutual agreement)
• Goal: Publish research at leading robotics/AI conferences and journals

The pay range for this internship position will be 6-8K per month.

MERL is seeking a highly motivated and qualified intern to contribute to the next generation of adaptive industrial robotics applications. The internship will focus on developing and validation of robust robotic disassembly solutions offering the opportunity to participate in cutting-edge research at the intersection of robotics, artificial intelligence, and sustainable manufacturing. There are several research topics of interest, including visual perception for disassembly guidance and planning, vision-language models for acting under uncertainty, and robot learning for manipulation. Applicants must be Ph.D. students with strong backgrounds in robot learning and computer vision. The selected intern will collaborate closely with MERL researchers to design and implement novel algorithms, conduct experiments, and disseminate research findings through a top-tier conference. The start date and duration are flexible, and interested applicants are encouraged to apply with an updated CV and a list of relevant publications.

Required Specific Experience

• Demonstrated experience in computer vision, robot learning, or vision-language models for robotic applications.

The pay range for this internship position will be 6-8K per month.

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. Experience in working with humanoids is

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: Summer 2026 (flexible based on mutual agreement)
• Goal: Publish research at leading robotics/AI conferences and journals

The pay range for this internship position will be 6-8K per month.

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 Vision-Language-Action (VLA) models and Foundation Models
• Strong proficiency with Python, PyTorch/JAX, 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!

The pay range for this internship position will be 6-8K per month.

We are looking for a graduate student interested in advancing the field of multimodal scene understanding, focusing on scene understanding using natural language for robot dialog and/or indoor monitoring with a large language model. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in deep learning for audio-visual, signal, and natural language processing. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with a flexible start date (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).

Required Specific Experience

• Experience with ROS2, C/C++, Python, and deep learning frameworks such as PyTorch are essential.

The pay range for this internship position will be 6-8K per month.

MERL is seeking a highly motivated intern to collaborate in the development decision making, planning and control for teams of heterogeneous robots (aerial, ground wheeled, legged etc.) in task such as inspection, monitoring and infrastructure repair. 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 Spring/Summer 2026 (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

The pay range for this internship position will be 6-8K per month.

MERL is seeking a self-motivated and highly qualified Ph.D. intern to contribute to the development of a safety-oriented active SLAM system for aerial robots. The work will involve the development of perception-aware safe planning algorithms, along with extensive validation in both simulation and on hardware, using drones equipped with onboard cameras.

The intern will work closely with MERL researchers in robotics and autonomy. The internship is expected to lead to a publication in a top-tier robotics, computer vision, or control conference and/or journal. The position has a flexible start date (Summer/Fall 2026) and a duration of 3–6 months.

Required Specific Experience

• Current enrollment in a Ph.D. program in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Computer Science, or a closely related field, with a focus on Robotics, Computer Vision, and/or Control Systems.
• Hands-on experience with aerial robots, including real-world flight testing.
• Expertise in one or more of the following areas: active SLAM; 3D computer vision; coverage path planning; multi-agent pathfinding; perception-aware planning.
• Excellent programming skills in Python and/or C++, with prior experience using ROS2 and high-fidelity simulators such as Isaac Sim and/or MuJoCo.
• A strong publication record or demonstrated research potential in leading computer vision or robotics venues, such as ICRA, IROS, RSS, RA-L, T-RO, CVPR, ECCV, ICCV, or NeurIPS.

Preferred Experience

• Strong software engineering skills, demonstrated through a publicly accessible codebase (e.g., GitHub or GitLab). Applicants are required to provide links to representative repositories.
• Experience with onboard perception, visual-inertial systems, or safety-critical autonomy.
• Familiarity with trajectory optimization, MPC, or optimization-based control for robots.

The pay range for this internship position will be 6-8K per month.

MERL seeks a self-motivated graduate student to conduct research on Visual Simultaneous Localization and Mapping (V-SLAM). Depending on the candidate’s expertise and interests, the internship may focus on 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 is a PhD student with a strong foundation in 3D computer vision and proficient programming skills in C/C++ and/or Python. Applicants should have at least one publication in a premier computer vision, machine learning, or robotics conference, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS.

The intern will collaborate with MERL researchers to develop and implement novel algorithms for V-SLAM, perform experiments, and document research outcomes. The work is expected to lead to a submission to a top-tier conference. The start date and internship duration are flexible.

Required Specific Experience

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

The pay range for this internship position will be 6-8K per month.

MERL seeks a motivated graduate student to conduct research in robust estimation for computer vision. Depending on the candidate’s background and interests, the internship may involve topics such as — but not limited to — camera pose estimation, 3D registration, camera calibration, pose-graph optimization, or transformation averaging.

The ideal applicant is a PhD student with strong expertise in 3D computer vision, RANSAC, or graduated non-convexity algorithms, along with solid programming skills in C/C++ and/or Python. Candidates should have at least one publication in a leading computer vision, machine learning, or robotics venue (e.g., CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS).

The intern will work closely with MERL researchers to develop and implement new algorithms for visual SLAM (V-SLAM), perform experiments, and document results. The goal is to produce work suitable for submission to a top-tier conference. The start date and duration of the internship are flexible.

Required Specific Experience

• Demonstrated experience in 3D computer vision, RANSAC, or graduated non-convexity algorithms for vision applications.

The pay range for this internship position will be 6-8K per month.

MERL is looking for a self-motivated intern to research on problems related to complex physical reasoning using digital twins and large vision-and-language models (VLMs). An ideal intern would be a Ph.D. student with a strong background in computer vision, machine learning, and robotics, with broad experience in using state-of-the-art physics engines. The candidate must have a strong background in 3D computer vision and machine learning (specifically in robotics and reinforcement learning), operational knowledge in using VLMs and generative AI, and experience in solving physical reasoning problems. Prior experience training VLMs would be a strong plus. The intern is expected to collaborate with researchers from multiple teams at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience with state-of-the-art physics simulators (both differentiable and non-differentiable)
• Experience in neuro-physical reasoning approaches
• Experience in state-of-the-art large vision-and-language models and generative AI models
• Enrolled in a PhD program
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

The pay range for this internship position will be 6-8K per month.

MERL is seeking a highly motivated and qualified individual to conduct research on fast/robust whole-body motion planning and control of mobile manipulators for agility, safety and precision. The ideal candidate should demonstrate solid background and track record of publications in the areas of robotic dynamics, motion planning, and control. Strong C++ and Python coding skills, knowledge of robotic software such as Pinocchio/Pybullet/MuJoCo, and optimization tools such as CasADi/PyTorch are a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is around summer 2026 and the duration is about 3 months.

Required Specific Experience

• Experience with robotic software such as Pinocchio/Pybullet/MuJoCo/ROS
• Strong C++ and Python coding skills
• Optimization tools such as CasADi/PyTorch

The pay range for this internship position will be 6-8K per month.

MERL is seeking an intern to work on mathematical modeling of manufacturing processes. The ideal candidate will have a strong background in process modeling with Petri nets and other methods, process simulation, and programming in C\C++, python and or other domain specific modeling languages. Experience programming for embedded Linux environments and experience with programmable logic controllers is highly desirable. The internship start date is flexible and the duration is 3-4 months.

Required Specific Experience

• Process modeling with Petri nets
• C\C++, Python

The pay range for this internship position will be 6-8K per month.