Robotics

MERL is looking for a highly motivated individual to work on designing robot motor skills for contact-rich dexterous manipulation such as tool manipulation using visuotactile sensing. Through the internship, we develop closed-loop controller for dexterous manipulation that can stabilize manipulation even under unexpected contact events. A successful internship will result in the submission of results to a peer-reviewed robotics conference papers (e.g., RSS, ICRA, CoRL) or journal in collaboration with MERL researchers.

The expected duration of this internship is around 3 months with a start date in the Summer/Fall of 2025. This internship will be onsite at MERL.

Required Specific Experience

• Senior PhD students in robotics and engineering related field.

• Either (1) experience in machine learning for robotic manipulation such as reinforcement, representation, and imitation learning, with an emphasis on real-world deployments, or (2) experience in model-based optimization for contact-rich robotic manipulation such as trajectory optimization, MPC, and estimators.

• Experience in visuotactile sensors (e.g., GelSight, GelSlim, DIGIT)

• Familiarity with related frameworks in either (1) Machine Learning (e.g., PyTorch), or (2) model-based optimization (e.g., IPOPT, Gurobi).

• Experience working with physical hardware systems is required.

• Prior publication experience in robotics venues such as ICRA, RSS, IROS, and CoRL.

Additional Desired Experience

• Experience in computer vision.

• Experience in dexterous manipulation such as bimanual manipulation, tool manipulation, and whole-body manipulation.

• Familiarity with robotic simulators (e.g., MuJoCo).

• Experience in scalable software development (e.g., CI, unittests, OOP) in Python or C++.

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 is expected, and in particular with deep learning algorithms for image processing and object detection. 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. 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. 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, with a flexible starting date.

Required Specific Experience

• Python, PyTorch, OpenCV

MERL is excited to announce an internship opening for a talented researcher to join our team. We are looking for an individual to contribute to cutting-edge research in human-machine interfaces (HMI) using multi-modal bio-sensors. This is an exciting opportunity to make a real impact in the field of human-machine interaction and biosignal processing, with the aim of publishing at leading research venues.

Ideal Candidate:

• Experienced PhD student or post-graduate researcher
• Strong background in brain-machine interface (BMI)
• Proficient in deep learning and mixed reality (XR)
• Skilled in robot manipulation, bionics, and bio sensing
• Digital modeling of human and environment
• Hands-on experience in Unity3d, ROS, OpenBCI, and XR headsets

If you are passionate about advancing technology in these areas, we encourage you to apply and be part of our innovative research team!