Artificial Intelligence

MERL is seeking interns passionate about robotics to contribute to the development of an Active Simultaneous Localization and Mapping (Active SLAM) framework guided by Large Language Models (LLM). The core objective is to achieve autonomous behavior for mobile robots. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms, such as legged and wheeled robots. The expectation at the end of the internship is a publication at a top-tier robotic or computer vision conference and/or journal.

The internship has a flexible start date (Spring/Summer 2025), with a duration of 3-6 months depending on agreed scope and intermediate progress.

Required Specific Experience

• Current/Past Enrollment in a PhD Program in Computer Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, or related field
• Experience with employing and fine-tuning LLM and/or Visual Language Models (VLM) for high-level context-aware planning and navigation
• 2+ years experience with 3D computer vision (e.g., point cloud, voxels, camera pose estimation) and mapping, filter-based methods (e.g., EKF), and in at least some of: motion planning algorithms, factor graphs, control, and optimization
• Excellent programming skills in Python and/or C/C++, with prior knowledge in ROS2 and high-fidelity simulators such as Gazebo, Isaac Lab, and/or Mujoco

Additional Desired Experience

• Prior experience with implementation and/or development of SLAM algorithms on robotic hardware, including acquisition, processing, and fusion of multimodal sensor data such as proprioceptive and exteroceptive sensors

MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.

Required Specific Experience

• Experience with multi-modal sensor fusion.

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.