Biography
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.
Research Interests
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?
Research Groups
Current Projects
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.
DPhil Opportunities
I am open to supervising DPhil students with an interest in legged locomotion, motion planning, trajectory optimization, skill representation and learning by demonstration.
Recent Publications
Curriculum-based reinforcement learning for quadrupedal jumping: a reference-free design
Atanassov V, Ding J, Kober J, Havoutis I & Santina CD (2024), IEEE Robotics and Automation Magazine, 2-15
Offline Adaptation of Quadruped Locomotion using Diffusion Models
O'Mahoney R, Mitchell AL, Yu W, Posner I & Havoutis I (2024)
BibTeX
@misc{offlineadaptati-2024/11,
title={Offline Adaptation of Quadruped Locomotion using Diffusion Models},
author={O'Mahoney R, Mitchell AL, Yu W, Posner I & Havoutis I},
year = "2024"
}
Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning
Atanassov V, Yu W, Mitchell AL, Finean MN & Havoutis I (2024)
BibTeX
@misc{constrainedskil-2024/10,
title={Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning},
author={Atanassov V, Yu W, Mitchell AL, Finean MN & Havoutis I},
year = "2024"
}
InteLiPlan: Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy
Ly KT, Lu K & Havoutis I (2024)
BibTeX
@misc{inteliplaninter-2024/9,
title={InteLiPlan: Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy},
author={Ly KT, Lu K & Havoutis I},
year = "2024"
}
R-LGP: a reachability-guided logic-geometric programming framework for optimal task and motion planning on mobile manipulators
Ly KT, Semenov V, Risiglione M, Merkt W & Havoutis I (2024), 2024 IEEE International Conference on Robotics and Automation (ICRA), 14917-14923
BibTeX
@inproceedings{rlgpareachabili-2024/8,
title={R-LGP: a reachability-guided logic-geometric programming framework for optimal task and motion planning on mobile manipulators},
author={Ly KT, Semenov V, Risiglione M, Merkt W & Havoutis I},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA 2024)},
pages={14917-14923},
year = "2024"
}
Learning and deploying robust locomotion policies with minimal dynamics randomization
Campanaro L, Gangapurwala S, Merkt W & Havoutis I (2024), Proceedings of the 6th Annual Learning for Dynamics and Control Conference, 578-590
BibTeX
@inproceedings{learninganddepl-2024/7,
title={Learning and deploying robust locomotion policies with minimal dynamics randomization},
author={Campanaro L, Gangapurwala S, Merkt W & Havoutis I},
booktitle={6th Annual Learning for Dynamics and Control Conference (L4DC 2024)},
pages={578-590},
year = "2024"
}
Oscillating latent dynamics in robot systems during walking and reaching
Parker Jones O, Mitchell A, Yamada J, Merkt W, Geisert M et al. (2024), Scientific Reports, 14(1)
Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion
Mitchell AL, Merkt W, Papatheodorou A, Havoutis I & Posner I (2024)
Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design
Atanassov V, Ding J, Kober J, Havoutis I & Della Santina C (2024)