Dr Ioannis Havoutis holds a PhD (2011) and an MSc (2007) from the University of Edinburgh, where he worked on machine learning for motion planning and control of articulated robotic systems.
Ioannis moved to Oxford in March 2017. Previously, he was a postdoctoral researcher at the Robot Learning and Interaction Group of the Idiap Research Institute, Switzerland, where he worked on online learning of complex skills from demonstration. Before this, Ioannis held a senior postdoctoral position in the Dynamic Legged Systems Lab, at the Advanced Robotics Department of the Italian Institute of Technology. There he led the Locomotion Group within the HyQ team, focusing on dynamic motion planning and control for legged locomotion.
Dr Havoutis’ research focuses the combination of machine learning with dynamic whole-body motion planning and control, targeting robots with arms and legs. Building on his previous work, Ioannis is interested both in the locomotion and manipulation aspects of autonomous robots.
He is a co-lead of the Dynamic Robotic Systems (DRS) group -- an integrated research lab within the Oxford Robotics Institute. He lead the research direction of robotic legged locomotion, designing and implementing of algorithms that enable autonomous legged mobility. His focus is on approaches for dynamic whole-body motion planning and control that allow robots with legs to robustly operate in a variety of challenging domains.
His work aims to answer questions such as: which path should the robot choose to reach its goal? how should the body and the individual joints of the robot move? which footholds and potential handholds maximize the robustness of the locomotion behaviour?
EPSRC-UKRI, Robotics and AI in Nuclear (RAIN) research hub
Field inspection of walking robots in sites such as Sellafield and Fukishima with radiation sensors.
EPSRC-UKRI, Off-Shore Robotics for Certification of assets (ORCA) research hub
Mobile robot mapping for inspection of industrial facilities with a variety of platforms.
EU H2020, MEMMO: Memory of Motion
Learning motion representations to enable fast dynamic trajectory optimization and re-planning.
EU H2020, THING: subTerranean Haptic INvestiGator
Haptic sensing, control and estimation for ANYmal.
EPSRC, New Investigator Award
Robust legged locomotion for autonomous mobility in challenging environments.
I am open to supervising DPhil students with an interest in legged locomotion, motion planning, trajectory optimization, skill representation and learning by demonstration.