We are looking for a graduate student interested in helping advance the field of multimodal scene understanding, with a focus on scene understanding using natural language for robot dialog and/or indoor monitoring using 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 flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

MERL is seeking an intern to work on research for human-machine interface with multi-modal bio-sensors. The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in brain-machine interface (BMI), deep learning, mixed reality (XR), remote robot manipulation, bionics, and bio sensing. The expected duration of the internship is 3-6 months, with a flexible start date.

MERL is looking for a highly motivated and qualified individual to work on optimization-based algorithms for coordination, scheduling, and motion planning of automated ground robots in uncertain surrounding environments. The ideal candidate should have experience in either one or multiple of the following topics: formulation of mixed-logic constraints as mixed-integer programs, connected vehicles and coordination, inter-robot collision avoidance, multi-agent scheduling, and traffic control systems. Prior experience with one or multiple traffic and/or multi-vehicle simulators (e.g., SUMO, CarSim, CARLA, etc.) is a plus. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on trajectory generation and motion planning for heterogenous teams of mobile robots, including drones and ground vehicles, with performance and safety guarantees. The ideal candidate is expected to be working towards a PhD with strong emphasis in planning and control, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, set-based methods in control (reachability, invariance), stochastic control for uncertain systems, SLAM and vision-based planning and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

MERL is looking for a highly motivated individual to develop and validate map building algorithms for autonomous mobile robots. The ideal candidate will have published in one or more of these topics: planning and control of ground robots, map building, (visual) SLAM, and sensor fusion. The candidate should be proficient in ROS and C/C++, familiar with Python, and has demonstrable experience working with mobile robots. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems for resource monitoring. The ideal candidate has experience in multi-agent motion planning and data-driven, sequential decision-making. The ideal candidate will have published in one or more of these topics: planning over discrete spaces, statistical estimation and hypothesis testing, reinforcement learning, and planning and control of aerial and ground robots. The candidate should be proficient in Python. Additional knowledge of ROS and C/C++ and demonstrable experience in ground and aerial robots are a plus. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on control and planning algorithms for legged robots for support activities of and collaboration with humans. The ideal candidate is expected to be working towards a PhD with strong emphasis in robotics control and planning and to have interest and background in as many as possible of: motion planning algorithms, control for legged robot locomotions, legged robots, perception and sensing with multiple sensors, SLAM, vision-based control. Good programming skills in Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.

MERL is seeking a highly motivated and qualified individual to conduct research in dynamic model-based robotic system design and control. The ideal candidate should demonstrate solid research record in robotic dynamics and differentiable simulation, motion planning and control, and optimization. Strong coding skill on implementing robotic dynamics and differentiable simulation/optimization using CasADi/PyTorch is a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

MERL is looking for a highly motivated individual to work on long-horizon planning and decision making for robotic manipulation using large language models (LLMs) and Hierarchical RL. The research will develop novel algorithms for long-horizon task planning and execution using Hierarchical Reinforcement learning and LLMs. The ideal candidate should have experience in either one or multiple of the following topics: (Deep) Reinforcement learning, Hierarchical RL, LLMs, policy optimization and Markov Decision Processes (MDPs). Senior PhD students in machine learning and engineering with a focus on Reinforcement Learning and robotics are encouraged to apply. Prior experience working with physics engines like Mujoco, Isaac Gym, etc. is required. Prior experience working with Python and ROS is required. 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 flexible start dates. This internship is preferred to be onsite at MERL.

MERL is seeking a highly motivated and qualified intern to work on deep learning for visual feedback in robotic manipulation. The ideal candidate would be a Ph.D. student with a strong background in deep learning and robotic manipulation. Several topics are available for consideration, including Object Pose Estimation, Goal-driven Grasping, Diffusion policy for Industrial Tasks, and Deformable Object Manipulation. The project requires the development of novel algorithms with implementation and evaluation on a robotic platform. Preferred qualifications include experience working with a physics engine simulator like PyBullet, Isaac Gym, or Mujoco, proficiency in Python programming, and experience with ROS. The successful candidate will collaborate with MERL researchers, and publication of relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and a list of publications in related topics

MERL is looking for a highly motivated and qualified intern to work on human-robot interaction (HRI) research. The ideal candidate would be a Ph.D. student with a strong background in HRI, focusing on robotic manipulation, deep learning, probabilistic modeling, or reinforcement learning. Several topics are available for consideration, including Intent Recognition in Multi-Object Scenes, Shared Autonomy, Cooperative Manipulation, Human-Robot Handovers, and Representation Learning for HRI. Experience working with robotics hardware and physics engine simulators like PyBullet, Issac Gym, or Mujoco is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

MERL is looking for a self-motivated and qualified candidate to work on human-robot-interaction for manipulation and assembly collaborative scenarios. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Control, estimation and perception for Robotic manipulation 2) Task and Motion Planning 3) Learning from demonstration algorithms applied to robotic manipulation 4) Machine learning techniques for modeling and control as well as regression and classification problems. 5) Experience in working with robotic systems and familiarity with physics engine simulators like Mujoco, Isaac Gym, PyBullet. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex manipulation tasks that involve human and robot collaborations. Proficiency in Python and ROS are required. The expectation is that the research will lead to one or more scientific publications. The expected duration s 3-4 months, with a flexible starting date.

MERL is offering a new research internship opportunity in the field of robotic manipulation. The position requires a robotics background, excellent programming skills and experience with Deep RL and Computer Vision. The position is open to graduate students on a PhD track only, and the length of the internship is three months with the possibility of extending if required. The intern is expected to disseminate this research in top tier scientific conferences such as RSS, IROS, ICRA etc., and if applicable, help with filing associated patents. Start and end dates are flexible.

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 and PyTorch are required for the position. Some 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 starting date of the internship is flexible, and applications outside of the peak summer season are encouraged, too.