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 intern to work on deep learning methods for detection and pose estimation of objects using vision and tactile sensing, in manufacturing and assembly environments. This role involves developing, fine-tuning and deploying models on existing hardware. The method will be applied for robotic manipulation where the knowledge of accurate position and orientation of objects within the scene would allow the robot to interact with the objects. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for pose estimation and tracking of objects. 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 Computer Vision and Robotic Manipulation.
• Experience with ROS and deep learning frameworks such as PyTorch are essential.
• Strong programming skills in Python.
• Experience with simulation tools, such as PyBullet, Issac Lab, or MuJoCo.

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