Dr Rahul Shome

Ph.D.
Visiting Fellow and Incoming Tenure-Track Lecturer
College of Engineering & Computer Science

Areas of expertise

  • Intelligent Robotics 460205
  • Autonomous Agents And Multiagent Systems 460202
  • Planning And Decision Making 460209
  • Social Robotics 460810

Research interests

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

Biography

Rahul Shome is an incoming tenured-track lecturer in the School of Computing at the Australian National University. He is a researcher in Robotics & AI who has been working 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 have published one book chapter in the Encyclopedia of 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 as on the organizing, chairing, and reviewer boards of major international robotics conferences and journals. 

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. 

Return to top

Updated:  28 September 2022 / Responsible Officer:  Director (Research Services Division) / Page Contact:  Researchers