We use robotics, AI and sensing to reveal how animals move, perceive and survive, then translate those discoveries into new machines and tools for a better world.
Fin joined REBL as a PhD student.
Karol joined REBL as a research engineer.
Amir awarded ARIA Grant: Amir's project, The Ecological Stethoscope, was awarded the Engineering Ecosystem Resilience Seed Creators' Grant from Advanced Research + Invention Agency (ARIA).
Jiaye joined REBL as a PhD student.
Amir at ICRA 2026: Amir gave a keynote on "Brain Circulation: The Diaspora's Role in African Robotics", at the Robotic Systems Designed for Underrepresented Communities event as part of the Community Building Day at IEEE International Conference on Robotics and Automation (ICRA) 2026.
REBL appeared in Rae Harbird Day: Shengyang and Michael gave lectures to KS3 students as part of the UCL CS flagship outreach event.
Michael joined REBL as a PhD student.
Shengyang joined REBL as a PhD student.
REBL combines robotics, AI, biomechanics and field biology to uncover how animals move, sense and survive.
We work in a loop. Measurements of real animals become models, models become predictions we can test, and robots put those predictions back into the physical world — where the next round of measurements begins. Six capabilities carry the work around that loop.
Cameras, thermal and inertial sensors capture animals in the wild without touching them.
Computer vision and probabilistic filters turn noisy field recordings into 3D motion.
Feedback laws that keep a body upright, on course and stable at speed.
Mechanical models of skeletons, muscles and contact that explain the forces behind a stride.
Trajectory optimisation and learning ask why an animal moves this way and not another.
Legged platforms test the answers in hardware, where physics has the final say.
Our projects combine field observations, computational models and physical experiments to answer specific questions about animals and create new technologies.
Can we measure the health of wildlife remotely and without contact?
Vital signs are among the first things to change when an animal is stressed or unwell, but measuring them normally means capture and sedation. We combine thermal imaging, long-range video and physics-informed models of heat transfer through fur to recover those signals at a distance, so that animals can be screened without ever being touched.
Can we reconstruct complete animal motion outside the controlled laboratory?
Laboratory motion capture cannot follow a cheetah across open ground. Through AcinoSet and WildPose we pair solid-state LiDAR with synchronised high-speed cameras to reconstruct full 3D body and limb motion in the field, giving biomechanics the kind of measurement that was previously only possible indoors.
What control strategies explain the cheetah’s exceptional acceleration, braking and turning?
The cheetah accelerates, brakes and turns harder than any legged robot. We use trajectory optimisation, inverse reinforcement learning and physical robot platforms — including a tail-and-spine “half-cheetah” — to work out which strategies the animal is actually using, and what a machine would have to do to match it.
Can spider webs inspire a new class of distributed acoustic sensors?
A spider’s web is already a sensor: the animal reads prey, wind and intruders from vibrations traveling along the silk. We are asking whether an engineered web can do the same — a lightweight, largely passive array that picks up sound and movement across a wide area without power-hungry electronics at every node.
A small group of computer scientists, engineers and biologists working across disciplinary boundaries.
We bring together robotics, AI, biomechanics and wildlife science to study questions no single discipline can answer alone.
Associate Professor in Robotics/AI · UCL CS
I am an engineer who accidentally became a biologist. I use robotics to ask questions about animals that conventional biological tools cannot answer.
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 translates 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 GEE & NERC AI-INTERVENE CDT
Computer vision for cheetah monitoring and individual re-identification.
Collaborator · Engineer at Opti-Num Solutions (12/2025–08/2026)
Head stabilisation and tracking of running cheetah.
Worked with REBL from Cape Town on the vision and control side of head stabilisation.
MEng Robotics and AI student · UCL CS (06/2026–08/2026)
Aerodynamics of Cheetah whisker sensing.
Undergraduate student · UCL Biosciences (01/2026–06/2026)
Cheetah 3D pose estimation.
REBL produces scientific findings, open datasets, software, sensing systems and robotic platforms.
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A 3D pose estimation dataset and baseline models for cheetahs in the wild.
The public repository describes the camera-calibration and 3D pose-estimation tools and the underlying 119,490-frame dataset.
A long-range 3D wildlife motion capture system.
Camera calibration, keypoint processing and 3D trajectory-estimation tools for animal motion capture.
Official implementation of the long-range wildlife motion-capture system.
Physical platforms are how we test what the animal data suggests. Each machine isolates one question about agility — and answering it usually sends us back to the cheetah with a sharper question.
The tailed robot that started the line.
A fast wheeled platform, deliberately built top-heavy enough to topple, carrying a single actuated tail. It tested the hypothesis drawn from pursuit footage: that a cheetah swings its tail to counter the rolling moment of a hard turn. Through the same 30° turning step Dima flipped at about 3.1 m/s without the tail and held the turn at 7.5 m/s with it. Later revisions added a second tail degree of freedom to reproduce the cheetah's conical tail motion, and AeroDima replaced the heavy inertial tail with a light, drag-based aerodynamic one.
Developed in collaboration with the University of Cape Town.
A bipedal robot for studying rapid manoeuvrability.
Where Dima asked what a tail is for, Baleka asks what the legs have to do. It is a 14 kg planar biped on quasi-direct-drive five-bar legs, its geometry chosen by trajectory optimisation and built for exactly the transient manoeuvres robotics usually designs around — sprinting from standstill and braking hard to a stop, rather than holding a steady gait. Hopping on both legs it reaches a vertical agility of 1.86 m/s; on a single leg it still exceeds the human figure of 0.89 m/s.
Developed in collaboration with the University of Cape Town.
A hybrid quadruped, and our platform for the cheetah's spine.
Kemba splits its actuation the way the animal data suggested: pneumatic cylinders at the knees for explosive push-off, electric motors at the hips for fine control. Driven by trajectory optimisation that models the pneumatics, it jumps roughly 2.2 times its own leg length. It is now our platform for spine morphology: in optimisation, a translational spine beats rigid and bending backs, lengthening the stride and storing energy along its axis, and it holds that advantage within Kemba's real actuator limits.
Developed in collaboration with the University of Cape Town.
Baleka's legs, with a spine and a tail.
A cheetah does not run on a rigid back, and it does not turn with its legs alone. Spaleka is the newest platform in the line, built to study the spine and the tail together in hardware: Baleka's leg design, joined by an articulated spine and fitted with a tail.
The spine lets the translational-spine result be measured on a real machine, against a rigid-backed baseline, rather than inferred from optimisation alone. The tail picks up where Dima left off: Spaleka will be used to study how the cheetah's tail steers its hindquarters through a manoeuvre.
Developed in collaboration with the University of Cape Town.
We welcome prospective researchers, biological collaborators, conservation partners and organisations interested in building new tools for understanding animals.
The lab has no vacancies right now. New roles are announced here and on the UCL jobs site. We are still glad to hear from prospective PhD students, visiting researchers and collaborators — get in touch through the contact page.
We are looking for motivated UCL students for various research projects, contact us for more information.
Bring robotics and biology into the same room.
