His research primarily focuses on effective planning algorithms that robots can use for intelligent problem-solving in human-centric environments. His focus is to design solutions to real-world robotics problems with practical performance, theoretical guarantees, that address human-centric objectives.

Keywords: robotics, artificial intelligence, task and motion planning, manipulation, multi-robot planning, collaborative robotics, human-robot interactions

Rahul Shome is a tenure track lecturer in the School of Computing at the Australian National University. He is a researcher in Robotics & AI. He has worked with Prof. Lydia E. Kavraki as a postdoctoral research associate and Fellow of the Rice Academy at Rice University. He earned his Ph.D. and M.S. degrees at Rutgers University, advised by Prof. Kostas E. Bekris. He has published a chapter in the Encyclopedia of Robotics, has been invited to contribute to the Foundations and Trends in Robotics, multiple peer-reviewed journal articles, and refereed international AI and robotics conference publications. His publications have garnered a best paper award at IEEE MRS and a nomination for best paper in automation award at IEEE ICRA among other recognitions. He has served on the organizing, chairing, and reviewer boards of major international robotics conferences and journals. He currently serves as an Associate Editor of the IEEE Robotics and Automation Letters journal.

He has experience in a broad set of cutting-edge robotics domains - multi-robot planning, multi-robot object rearrangement problems, task and motion planning, manipulation, and sampling-based planning algorithms. He has engaged in widely recognized work on these problems. His future research projects will touch upon core robotics contributions with connections to technical areas including design and analysis of algorithms, stochastic decision making, topology, combinatorics, optimization, machine learning, control theory, sensing and perception, hardware design, and human-robot interactions.

I currently have openings for PhD positions and research positions in robotics planning with a focus on collaborative automation applications. The research direction will enable the student to cover topics in ranging across robotics, AI, task and motion planning, control, sensing and perception, learning, probabilistic techniques, formal methods, and human-robot interactions. Feel free to reach out if you are interested in engaging in cutting edge research to build towards the next generation of robotics and AI capabilities.