Doctoral student position at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology.
In this project, we will develop simplicial complex-based robot configuration space models for embodiment-aware robot motion planning and learning from demonstration. We will develop extremely large scale and detailed data-driven simplicial complex-based models of robot arm configuration spaces that represent the complex geometry that they exhibit. Based upon these representations we will develop algorithms for efficient updates, optimal motion planning and probabilistic reasoning for robotic manipulation. The project will be highly interdisciplinary as it lies at the intersection of Computational Geometry, Motion Planning, Bayesian Machine Learning and Optimal Control.
The position is at the division of Robotics, Perception and Learning (RPL). Read more: https://www.kth.se/is/rpl/
The position is part of and funded by WASP. The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry.