Or0087: Internship - Human-Robot Collaboration With Shared Autonomy Milano

Or0087: Internship - Human-Robot Collaboration With Shared Autonomy
Milano
Lombardia, Milano

MERL is seeking a highly motivated and qualified individual to conduct research in safe/robust whole-body motion planning and control of mobile manipulators.

The ideal candidate should demonstrate a 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 flexible and the duration is about 3 months.

Required Specific Experience Solid background and track record of conducting innovative research in the dynamic modeling, motion planning, and control of robotic systems. Experience with C++/Python, Pinocchio, Pybullet, MuJoCo, CasADi, PyTorch. 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.

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. We offer an interdisciplinary and collaborative research environment with excellent benefits where talented people apply their skills and grow professionally.

We offer many opportunities to participate in exciting research in a stimulating environment with the likelihood of a publication as a result.

Look deeper into significant research at MERL:

Sustainable AIPS-NeuS: A Probability-guided Sampler for Neural Implicit Surface RenderingGear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-Aware Spatio-Temporal SamplingTI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion ModelsGeneration or Replication: Auscultating Audio Latent Diffusion ModelsMERL is making Robotics software and data available to the research community:

Lagrangian Inspired Polynomial for Robot Inverse Dynamics (LIP4RobotInverseDynamics)Python-based Robotic Control & Optimization Package (PyRoboCOP)Monte Carlo Probabilistic Inference for Learning COntrol (MC-PILCO)Online Feature Extractor Network (OFENet)

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