Locomotion

Walking, Climbing, Slithering, Swimming

Nature has solved locomotion across every conceivable substrate. While traditional wheeled robots struggle with stairs, rubble, and soft ground, animals move effortlessly through diverse landscapes. Biomimetic robotics translates these biological gait patterns into mechanical and control systems.

Quadrupedal Gaits — Dogs, Goats, Horses

The quadrupedal gait patterns of mammals — walk, trot, pace, canter, gallop — have been reverse-engineered into robots like Boston Dynamics' Spot and ETH Zurich's ANYmal. These platforms use torque-controlled actuators and model-predictive control to replicate the spring-mass dynamics of animal locomotion, achieving stability across ice, stairs, rock fields, and inclines up to 45 degrees. Key to their performance is the principle of dynamic walking: unlike statically stable walking (which keeps the center of mass over the support polygon at all times), animals and biomimetic quadrupeds use momentum and compliant legs to maintain stability through continuous falling-and-catch — a far more energy-efficient strategy.

Raibert, M., et al. "BigDog, the Rough-Terrain Quadruped Robot." IFAC Proceedings Volumes, 41(2), 2008. doi:10.3182/20080706-5-KR-1001.01833

Gecko Adhesion — Van der Waals Climbing

The gecko's ability to climb vertical glass surfaces — and hang from a single toe — has inspired robots with dry adhesive feet. Gecko setae (millions of microscopic keratin hairs) exploit van der Waals forces, creating adhesion through intermolecular attraction rather than suction, glue, or static charge. Biomimetic gecko robots from Stanford, NASA JPL, and Simon Fraser University use directional polymer stalks that engage when dragged downward and release when lifted — enabling climbing on glass, drywall, painted metal, and even Teflon. DARPA's "RoboClaw" and NASA's "Lemur III" have demonstrated gecko-adhesive grippers for satellite servicing and asteroid sample collection.

Autumn, K., et al. "Evidence for van der Waals Adhesion in Gecko Setae." Proceedings of the National Academy of Sciences, 99(19), 2002. doi:10.1073/pnas.192252799

Hawkes, E.W., et al. "Human climbing with a gecko-inspired adhesive robot." Science Robotics, 6(55), 2021. doi:10.1126/scirobotics.abi9187

Serpentine Locomotion — Snakes, Eels, Worms

The undulating motion of snakes has led to serpentine robots capable of navigating through gaps smaller than their own diameter, climbing pipes, and traversing rubble piles. Four primary gaits have been replicated: lateral undulation (side-to-side waves), concertina (anchoring and pulling), sidewinding (rolling diagonal lines), and rectilinear (caterpillar-like). These robots are deployed in search and rescue (Japan's Active Scope Camera), archaeological exploration, and industrial pipe inspection. The key insight: snakes use anisotropic friction — their scales slide more easily forward than backward — which has been replicated through passive directional treads.

Transeth, A.A., et al. "Snake Robot Obstacle-Aided Locomotion: Modeling, Simulation, and Experiments." IEEE Transactions on Robotics, 24(1), 2008. doi:10.1109/TRO.2007.913394

Bio-inspired Flight — Insects, Birds, Bats

While early attempts at human flight tried to replicate bird wing-flapping, modern drone technology draws more from insect flight patterns. Bee and fly wing mechanics — with their ability to hover, execute rapid directional changes, and recover from collisions — have informed micro air vehicles (MAVs) down to 3 cm wingspan. The key principles: unsteady aerodynamics (leading-edge vortices that generate lift at low Reynolds numbers), stroke-plane modulation (controlling flight direction by tilting the wing stroke plane rather than using separate control surfaces), and haltere-based stabilization (insects use modified hindwings as gyroscopic sensors, replicated in MEMS gyroscopes).

Dickinson, M.H., et al. "Wing Rotation and the Aerodynamic Basis of Insect Flight." Science, 284(5422), 1999. doi:10.1126/science.284.5422.1954

Sensory Systems

Echolocation, Compound Vision, and Lateral Lines

Biological sensory systems dramatically outperform engineered equivalents in size, power efficiency, and noise robustness. Biomimetic sensors translate these biological mechanisms into compact, low-power hardware.

Bat Echolocation — Biosonar for Autonomous Navigation

Bats emit ultrasonic pulses and interpret the returning echoes to build a 3D spatial map of their environment — in complete darkness, through foliage, and with enough resolution to discriminate insect species by wing-beat frequency. Biomimetic sonar systems replicate this through frequency-modulated chirps (linear sweeps from 80 kHz to 20 kHz) and binaural echo processing. Applications include autonomous vehicle navigation in GPS-denied environments, obstacle avoidance for drones in forest canopies, and assistive devices for visually impaired users. The key engineering insight: bats' pinnae (outer ears) are highly directional and asymmetrical, creating frequency-dependent beam patterns that encode elevation angle — a principle replicated in 3D-printed biomimetic ear baffles.

Schnitzler, H.U., & Kalko, E.K.V. "Echolocation by Insect-Eating Bats." BioScience, 51(7), 2001. doi:10.1641/0006-3568(2001)051[0557:EBIEB]2.0.CO;2

Compound Eyes — Wide-Field Motion Detection

Insect compound eyes consist of thousands of individual ommatidia, each providing a pixel of visual information. This architecture offers an extremely wide field of view (nearly 360 degrees in some species), high sensitivity to motion, and fast temporal response — at a fraction of the power and processing cost of conventional cameras. Biomimetic vision systems inspired by compound eyes use curved sensor arrays and spherical lenses to achieve panoramic field-of-view with minimal distortion. The "DragonEye" sensor — developed by the University of Adelaide and the University of Maryland — uses an array of 180 photodetectors on a curved surface to detect motion at 10,000 frames per second.

Song, Y.M., et al. "Digital cameras with designs inspired by the arthropod eye." Nature, 497(7447), 2013. doi:10.1038/nature12083

Fish Lateral Line — Hydrodynamic Sensing

The lateral line system of fish detects water movement, pressure gradients, and low-frequency vibrations — enabling schooling behavior, obstacle avoidance in murky water, and prey tracking in the absence of visual cues. Biomimetic lateral line sensors use arrays of MEMS pressure sensors and flow-velocity sensors arranged along the robot body to replicate this distributed sensing capability. The MIT RoboTuna and the University of Bath's robotic fish have demonstrated that lateral line feedback enables station-holding in turbulent flows and energy-efficient wake riding — behaviors directly inspired by fish schooling.

Triantafyllou, M.S., & Triantafyllou, G.S. "An Efficient Swimming Machine." Scientific American, 272(3), 1995. doi:10.1038/scientificamerican0395-64

Swarm Intelligence

Ant Colonies, Bee Hives, and Flocking Birds

Swarm robotics draws inspiration from collective behaviors observed throughout the natural world — where simple individual agents following local rules produce complex, adaptive group intelligence without centralized control.

Ant colony optimization (ACO) — ants deposit pheromone trails to mark paths to food sources; the pheromone concentration grows along shorter routes, creating positive feedback. Robotic swarms replicate this principle using virtual pheromones (RF signals, projected light patterns) to coordinate path planning, area coverage, and task allocation in unknown environments.

Bee foraging — honey bees perform a waggle dance to communicate the direction and distance of flower patches to nest-mates. This decentralized recruitment strategy has been adapted into algorithms for multi-robot task allocation, where robots that discover high-value targets broadcast location-bearing signals to recruit idle swarm members.

Bird flocking — Craig Reynolds' Boids model (1987) demonstrated that three simple rules (separation, alignment, cohesion) produce realistic flocking behavior. Drone swarms from Intel and the University of Pennsylvania's GRASP Lab use variants of this model to execute coordinated aerial maneuvers, formation flight, and collective mapping — without any individual drone having a global view of the swarm.

Bonabeau, E., et al. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, 1999. ISBN 978-0195131598.

Reynolds, C.W. "Flocks, Herds, and Schools: A Distributed Behavioral Model." ACM SIGGRAPH Computer Graphics, 21(4), 1987. doi:10.1145/37401.37406

Soft Robotics

Octopus Arms, Elephant Trunks, and Muscular Hydrostats

Traditional rigid robots excel at precision and speed but struggle with adaptability, safety, and manipulation in unstructured environments. Soft robotics — inspired by animals without rigid skeletons — offers a fundamentally different approach: machines made of compliant materials that deform to conform to their environment.

Octopus-inspired arms — the octopus arm is a muscular hydrostat: a structure with no rigid skeleton that achieves movement through muscle groups acting in opposition (longitudinal, transverse, and oblique). Biomimetic soft arms use pneumatic or tendon-driven actuators embedded in silicone elastomers to replicate this infinite-degree-of-freedom manipulation. These arms can grasp objects of arbitrary shape, squeeze through narrow openings, and apply distributed forces without concentrated pressure points — making them ideal for underwater manipulation, surgical assistance, and search operations in confined spaces.

Elephant trunk manipulators — the elephant trunk, with an estimated 40,000–50,000 muscles, achieves both gross manipulation (lifting 300 kg logs) and fine manipulation (picking up a single blade of grass). Soft robotic trunks use continuum kinematics — modeling the trunk as a series of bending segments with constant curvature — to replicate this combination of strength and dexterity. Applications include agricultural harvesting, warehouse picking of irregular objects, and assistive robotics.

ALT's synthetic tissues program directly supports soft robotics development. Our bio-inspired elastomers — engineered to match the mechanical properties of octopus muscle, mammalian smooth muscle, and cardiac tissue — provide the material foundation for next-generation soft actuators. By controlling durometer, anisotropy, and fatigue life at the material composition level, we enable soft robotic structures that move, grip, and sense more like living tissue than any pneumatic silicone actuator.

Laschi, C., et al. "Soft Robot Arm Inspired by the Octopus." Advanced Robotics, 26(7), 2012. doi:10.1163/156855312X626343

Calisti, M., et al. "Soft robotics in underwater environments." Soft Robotics, 5(2), 2018. doi:10.1089/soro.2017.0099

Machine Learning

Learning from Nature with Neural Networks

Machine learning has accelerated biomimetic robotics by enabling robots not just to copy biological forms, but to understand and adapt the underlying principles in real-time.

Neural locomotion controllers: Deep reinforcement learning (DRL) trained on animal movement data can generate gaits that adapt to terrain changes without explicit programming. The ANYmal robot taught itself to walk, trot, and recover from falls after 11 hours of simulated training — discovering gait patterns that closely match biological quadrupeds without ever being explicitly programmed to do so. The neural network learned the same spring-mass dynamics that evolution discovered, but in simulation rather than across millennia.

Evolutionary robotics: Genetic algorithms can optimize robotic morphology and control simultaneously — replicating the trial-and-error process that shaped life itself. Researchers at the University of Vermont and Wyss Institute have used evolutionary algorithms to design soft robots from scratch: starting with a random population of simulated soft body plans and selecting for locomotion speed, the system automatically discovered swimming, crawling, and rolling morphologies that were then 3D-printed and deployed. This "robot evolution" represents the tightest possible integration of biomimetic principles — not copying a specific biological form, but applying the same evolutionary process that created it.

Neural field controllers for soft robots: Controlling soft robots is fundamentally harder than rigid robots because the number of degrees of freedom approaches infinity. ALT's research uses neural field models — continuous neural networks that map spatial coordinates to actuator commands — to achieve precise control of continuum soft arms. These models are trained on FEM simulation data and fine-tuned on physical hardware, enabling soft robots to reach targets, follow paths, and apply forces with a fraction of the computational cost of traditional FEM-based control.

Chen, T., et al. "Neural-field-based control of continuum manipulators." IEEE International Conference on Soft Robotics (RoboSoft), 2024.

Kriegman, S., et al. "A scalable pipeline for designing reconfigurable organisms." Proceedings of the National Academy of Sciences, 117(4), 2020. doi:10.1073/pnas.1910837117

ALT Capabilities

Biomimetic Engineering at ALT

ALT integrates biomimetic principles across every layer of our engineering stack — from synthetic tissue materials that replicate biological soft tissue mechanics, to edge AI controllers that implement neural locomotion and swarm coordination.

References

Key Research & Further Reading

Autumn, K., et al. "Evidence for van der Waals Adhesion in Gecko Setae." PNAS, 99(19), 2002. doi:10.1073/pnas.192252799

Bonabeau, E., Dorigo, M., & Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, 1999.

Calisti, M., et al. "Soft robotics in underwater environments." Soft Robotics, 5(2), 2018.

Chen, T., et al. "Neural-field-based control of continuum manipulators." IEEE RoboSoft, 2024.

Dickinson, M.H., et al. "Wing Rotation and the Aerodynamic Basis of Insect Flight." Science, 284(5422), 1999.

Hawkes, E.W., et al. "Human climbing with a gecko-inspired adhesive robot." Science Robotics, 6(55), 2021.

Kriegman, S., et al. "A scalable pipeline for designing reconfigurable organisms." PNAS, 117(4), 2020.

Laschi, C., et al. "Soft Robot Arm Inspired by the Octopus." Advanced Robotics, 26(7), 2012.

Raibert, M., et al. "BigDog, the Rough-Terrain Quadruped Robot." IFAC Proceedings, 41(2), 2008.

Reynolds, C.W. "Flocks, Herds, and Schools: A Distributed Behavioral Model." ACM SIGGRAPH, 21(4), 1987.

Schnitzler, H.U., & Kalko, E.K.V. "Echolocation by Insect-Eating Bats." BioScience, 51(7), 2001.

Song, Y.M., et al. "Digital cameras with designs inspired by the arthropod eye." Nature, 497(7447), 2013.

Transeth, A.A., et al. "Snake Robot Obstacle-Aided Locomotion." IEEE Trans. Robotics, 24(1), 2008.

Triantafyllou, M.S., & Triantafyllou, G.S. "An Efficient Swimming Machine." Scientific American, 272(3), 1995.

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