Biography
Maurice Fallon is an Associate Professor and Royal Society University Research Fellowship. He is also a Research Fellow of Wolfson College. He leads the Dynamic Robot Systems Group within Oxford Robotics Institute. DRS develops algorithms for navigating quadrupeds, motion planning algorithms to allow quadrupeds to move more smoothly and accurately.
Prof. Fallon studied Electronic Engineering at University College Dublin. His PhD research in the field of acoustic source tracking was carried out in the Engineering Department of the University of Cambridge.
Immediately after his PhD he moved to MIT as a post-doc and later as a research scientist in the Marine Robotics Group (2008-2012) working in robot mapping. From 2012-2015 he was the perception lead of MIT's team in the DARPA Robotics Challenge – a multi-year competition developing technologies for semi-autonomous humanoid exploration and manipulation in disaster situations.
Recent Developments
The Oxford Robotics Institute are playing a key role in two of the four Robotics Hubs funded by the EPSRC as part of their £44.5 million investment over the next three and a half years into Robotics and Artificial intelligence (AI). More details here.
Researcher from Prof. Fallon's group contributed to Team Cerberus in the DARPA Subterranean Challenge - developing a team of robots to explore mines and caves. The finals of the 'SubT' were held in a huge mine in Louisville in September 2021. Cerberus won the top prize of $2m. More details here.
Most Recent Publications
Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry
Zhang L, Wisth D, Camurri M & Fallon M (2022), IEEE ROBOTICS AND AUTOMATION LETTERS, 7(2), 1182-1189
An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions
Mattamala M, Chebrolu N & Fallon M (2022), IEEE Robotics and Automation Letters
ICP Localization and Walking Experiments on a TALOS Humanoid Robot
Lasguignes T, Maroger I, Fallon M, Ramezani M, Marchionni L et al. (2021), 2021 20th International Conference on Advanced Robotics (ICAR)
Learning an expert skill-space for replanning dynamic quadruped locomotion over obstacles
Surovik D, Melon O, Geisert M, Fallon M & Havoutis I (2021), Proceedings of the 2020 Conference on Robot Learning, 1509-1518
Real-time trajectory adaptation for quadrupedal locomotion using deep reinforcement learning
Gangapurwala S, Geisert M, Orsolino R, Fallon M & Havoutis I (2021), Proceedings of the 2021 International Conference on Robotics and Automation (ICRA), 5973-5979
Research Interests
Prof. Fallon's research is focused on probabilistic methods for localization and mapping. His research focus on state estimation and mapping for dynamic robots (quadrupeds, handheld devices and even drones). He is also interested in dynamic motion planning and control. He focuses on developing methods which are robust in challenging situations (darkness, underground, outdoors) using probabilistic sensor fusion.
Current Projects
THING: Haptic locomotion and sensing for quadrupeds (EU H2020)
MEMMO: Generating complex robot motions with a Memory of Motion (EU H2020)
RAIN: Robotics and AI research for the Nuclear Industry (EPSRC Research Hub)
ORCA: Robotics for the Oil and Gas Industry (EPSRC Research Hub)
SubT: Team Cerberus in the DARPA Subterranean (SubT) Challenge
Research Groups
Most Recent Publications
Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry
Zhang L, Wisth D, Camurri M & Fallon M (2022), IEEE ROBOTICS AND AUTOMATION LETTERS, 7(2), 1182-1189
An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions
Mattamala M, Chebrolu N & Fallon M (2022), IEEE Robotics and Automation Letters
ICP Localization and Walking Experiments on a TALOS Humanoid Robot
Lasguignes T, Maroger I, Fallon M, Ramezani M, Marchionni L et al. (2021), 2021 20th International Conference on Advanced Robotics (ICAR)
Learning an expert skill-space for replanning dynamic quadruped locomotion over obstacles
Surovik D, Melon O, Geisert M, Fallon M & Havoutis I (2021), Proceedings of the 2020 Conference on Robot Learning, 1509-1518
Real-time trajectory adaptation for quadrupedal locomotion using deep reinforcement learning
Gangapurwala S, Geisert M, Orsolino R, Fallon M & Havoutis I (2021), Proceedings of the 2021 International Conference on Robotics and Automation (ICRA), 5973-5979
DPhil Opportunities
I am interested in supervising research students in navigation, mapping and motion planning for robots, particularly dynamic and/or legged robots.
Most Recent Publications
Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry
Zhang L, Wisth D, Camurri M & Fallon M (2022), IEEE ROBOTICS AND AUTOMATION LETTERS, 7(2), 1182-1189
An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions
Mattamala M, Chebrolu N & Fallon M (2022), IEEE Robotics and Automation Letters
ICP Localization and Walking Experiments on a TALOS Humanoid Robot
Lasguignes T, Maroger I, Fallon M, Ramezani M, Marchionni L et al. (2021), 2021 20th International Conference on Advanced Robotics (ICAR)
Learning an expert skill-space for replanning dynamic quadruped locomotion over obstacles
Surovik D, Melon O, Geisert M, Fallon M & Havoutis I (2021), Proceedings of the 2020 Conference on Robot Learning, 1509-1518
Real-time trajectory adaptation for quadrupedal locomotion using deep reinforcement learning
Gangapurwala S, Geisert M, Orsolino R, Fallon M & Havoutis I (2021), Proceedings of the 2021 International Conference on Robotics and Automation (ICRA), 5973-5979
