Robotics-Enabled Biology Lab (REBL) is a research group at University College London working at the intersection of robotics, sensing and biology.
We study how animals achieve agility and speed and then apply those insights to robotics.
We build physical models (robots) that mirror key biological features to study cheetah locomotion.
We develop non-invasive methods to study wild animals in unstructured environments.
A small group of computer scientists, engineers and biologists. Backgrounds in robotics, computer vision, machine learning, and sensing — all in one corridor in UCL East.
Associate Professor in Robotics/AI · UCL CS
Bio-inspired robotics, biomechanics, sensor fusion.
Prev. director at African Robotics Unit, University of Cape Town.
Amir is an Associate Professor at UCL, where he studies how animals achieve
agility and
speed and then apply those insights to robotics. His background is in mechatronics engineering, and he
began his career as a flight control engineer before moving into animal biomechanics. Over time, his
work
has expanded to include sensor fusion, computer vision, and optimal control—all aimed at measuring and
modeling locomotion in real-world conditions.
He collaborates with conservationists, biologists, and fellow engineers to understand factors like spine
flexibility and tail dynamics in animals. In parallel, he translate those biological principles into
more
capable robots, focusing on practical applications in fields such as ecology, disease monitoring, and
sports science. His research has been recognized by awards such as the Google Research Scholar Award and
the MathWorks Research Award, but his main motivation is to uncover how living systems move and adapt,
and to channel that knowledge toward useful, real-world technologies.
PhD student · UCL CS & MathWorks
Inverse reinforcement learning for cheetah locomotion.
Co-supervised with Prof. Dimitrios Kanoulas.
MRes: Imperial College London.
BEng: Harbin Institute of Technology.
PhD student · UCL CS & NERC AI-INTERVENE CDT
Multi-sensor fusion for remote wildlife health monitoring.
Co-supervised with Prof. Kate Johns.
MSc: Imperial College London.
BEng: University of Cape Town.
Collaborator · Engineer at Opti-Num Solutions
Head stabilisation and tracking of running cheetah.
A filterable list. We try to keep code, data and protocol descriptions alongside every paper — if a link is missing here it's a bug, not a policy.
Showing 5 of 5
ICRA · IEEE International Conference on Robotics and Automation · 2021
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@inproceedings{joska2021acinoset,
title = {{AcinoSet}: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild},
author = {Joska, Daniel and Clark, Liam and Muramatsu, Naoya and Jericevich, Ricardo and Nicolls, Fred and Mathis, Alexander and Patel, Amir},
booktitle = {IEEE International Conference on Robotics and Automation},
year = {2021}
}
Nature Protocols · 2019 · 1,783 citations
@article{nath2019deeplabcut,
title = {Using {DeepLabCut} for 3D markerless pose estimation across species and behaviors},
author = {Nath, Tanmay and Mathis, Alexander and Chen, An Chi and Patel, Amir and Bethge, Matthias and Mathis, Mackenzie Weygandt},
journal = {Nature Protocols},
year = {2019}
}
IEEE Robotics and Automation Letters · 2019
@article{patel2019contact,
title = {Contact-implicit trajectory optimization using orthogonal collocation},
author = {Patel, Amir and Shield, Stacey L. and Kazi, Shameeq and Johnson, Aaron M. and Biegler, Lorenz T.},
journal = {IEEE Robotics and Automation Letters},
year = {2019}
}
IEEE Transactions on Robotics · 2015
@article{patel2015conical,
title = {On the Conical Motion of a Two-Degree-of-Freedom Tail Inspired by the Cheetah},
author = {Patel, Amir and Boje, Edward},
journal = {IEEE Transactions on Robotics},
year = {2015}
}
IROS · IEEE/RSJ International Conference on Intelligent Robots and Systems · 2013
@inproceedings{patel2013rapid,
title = {Rapid turning at high-speed: Inspirations from the cheetah's tail},
author = {Patel, Amir and Braae, Mark},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2013}
}
Website established: V1 of the REBL website is now live.
REBL appeared in Rae Harbird Day: Shengyang and Michael gave lectures to KS3 students as part of the UCL CS flaship outreach event.
We are actively hiring at every level — postdoc, PhD, RSE, and short MSc / MEng projects for UCL students. Email the PI with a CV, a short statement of research interests, and (for PhD applicants) a paragraph on a project you'd want to lead.
Description of the postdoctoral researcher position.
Description of the research engineer position.
The following are open MSc/MEng thesis projects for UCL students. Click a project to read more, or download the PDF for the full description.
This project investigates whether solid-state LiDAR point clouds can be used to identify wildlife species and whether LiDAR intensity (reflectivity) provides information beyond 3D geometry. Using an existing Livox Tele-15 dataset collected on wildlife in South Africa, with potential extensions from UK partner zoos, the student will develop and compare geometry-only and reflectivity-aware classification methods using both classical point-cloud features and deep learning approaches. A key scientific question is whether reflectivity contains biologically meaningful information or primarily reflects sensor and environmental effects. The project aims to establish a benchmark for wildlife LiDAR recognition and evaluate the benefits of combining geometric and intensity information, with potential for publication in a sensing, computer vision, or robotics venue.
This project investigates the role of the cheetah’s flexible spine in agile locomotion using a reduced robotic “half-cheetah” platform developed by the African Robotics Unit at the University of Cape Town. The student will develop a stable galloping-inspired controller for the robot’s hindquarters and 3-DOF spine, building a Simscape model, deriving reduced-order dynamics, and deploying the controller on a Speedgoat real-time system. By comparing locked-spine and active-spine conditions using metrics such as stride consistency, disturbance rejection, body attitude recovery, and hindquarter re-alignment, the project aims to quantify the contribution of spinal motion to locomotor performance. The resulting platform will provide a validated baseline for future tail studies and a physical model for investigating cheetah neuromechanics.
This project investigates whether core body temperature can be estimated from thermal images using a physics-informed approach that accounts for heat transfer through insulating fur. The student will develop a controlled experimental platform consisting of a heated-core phantom, interchangeable fur layers, internal temperature sensors, and thermal imaging, before building a thermal inversion model to infer internal temperature from surface observations. The approach may combine physical heat-transfer modelling with learned parameter estimation or residual correction and will be benchmarked against conventional thermal-imaging and end-to-end machine learning methods. If successful, the method will be validated on canine data and potentially applied to existing cheetah datasets. The project aims to deliver a validated thermal test platform, a physics-informed temperature estimator, and a rigorous comparison with data-driven alternatives, with potential for publication in sensing, robotics, or animal-monitoring research.