Rapid and Reversible Morphing to Enable Multifunctionality in Robots

Abstract

Biological organisms are extraordinary in their ability to change physical form to perform different functions. Mimicking these capabilities in engineered systems has the potential to create multifunctional robots that adapt form and function on‐demand for search and rescue, environmental monitoring, and transportation. Organisms are able to navigate such unstructured environments with the ability to rapidly change shape, move swiftly in multiple locomotion modes, and do this efficiently and reversibly without external power sources, feats which are difficult for robots. Herein, a bio‐inspired latch‐mediated, spring‐actuated (LaMSA) morphing mechanism is harnessed to near‐instantaneously and reversibly reconfigure a multifunctional robot to achieve driving and flying configurations. This shape change coupled with a combined propeller/wheel leverages the same motors and electronics for both flying and driving, providing efficiency of morphing and locomotion for completely untethered operation. The adaptive robotic vehicle can move through confined spaces and rough terrain which are difficult to pass by driving or flying alone, and expands the potential range through power savings in the driving mode. This work provides a powerful scheme for LaMSA in robots, in which controlled, small‐scale LaMSA systems can be integrated as individual components to robots of all sizes to enable new functionalities and enhance performance.

Introduction

Transforming between physical states in biological organisms is energy efficient, rapid, and reversible, which provides functional diversity to achieve objectives such as traversing different terrain.^[1–3] These capabilities have inspired a push toward multifunctionality in engineered systems,^[4–9] such as various robots or machines that strive to mimic functions found in insects,^[10–13] birds,^[14,15] bats,^[16] and turtles.^[17] One promising approach to leverage such reconfigurations is shape-morphing robots,^[18,19] where different shapes are used to achieve different functions; the use of a shape change can help improve the performance by optimizing the form for a given task. Shape morphing has been performed in multi-modal robots using traditional motors^{[12,16,20–28]} or adaptive materials^[17,29,30] to cause a shape change for different locomotion modes. However, the morphing systems in these robots typically contrast the key aspects of biological systems, in that current engineered robots can have drawbacks such as a bulky form factor, high power usage, being tethered/connected to external power or data lines, or a long morphing duration.

One potential actuation strategy for morphing systems to overcome these challenges is using latch-mediated, spring actuated (LaMSA) systems, which have the potential to improve speed, scalability, and performance of multi-modal robots.^[31] LaMSA systems are based on the use of potential energy stored in springs and released by latch mechanisms to allow for movements with greater speed and power than traditional small scale actuation schemes^[32–34] LaMSA systems are found frequently in nature,^[35] in organisms such as the mantis shrimp,^[31,36] trap-jaw ant,^[37,38] and Venus fly trap,^[39–41] and have led to engineered systems with amplified power output and speed.^[33,42–44]

Here, we present a small, reconfigurable robotic vehicle that uses a LaMSA morphing system to undergo a shape change to achieve multiple locomotion modes (Figure 1a). Through the use of the bio-inspired LaMSA morphing system the shape change is rapid, reversible, and repeatable, so the robot may switch between its flying and driving states on-demand without external intervention. Our bistable LaMSA morphing mechanism is driven by an artificial muscle wire, which eliminates the need for traditional motors, enabling a compact and efficient system that is completely untethered during locomotion and morphing (Figure 1b). The robot uses a combined propeller/wheel which enables the use of a single propulsion system for multiple locomotion modes. This approach enables the robot to use its dual transport modes to navigate an environment which could not be readily completed by a conventional single mode robot that can only fly or drive (Figure 1c). The robot accomplishes this task while moving swiftly and efficiently through its environment with high velocities above 9 m s⁻¹ (>39 body lengths per second) in both the ground and flying states, and a very low cost of transport when driving (Figure 1c bottom plot for more details). The majority of the morphing between shapes takes under 50 ms with maximum angular morphing velocities above 2300° s⁻¹, with only 8.5 W of power applied for 1.5 s total (Figure 1d). This morphing is reversible over multiple cycles and power is not required to maintain either of the two locomotion states after a morph is completed. All electronic control and power delivery systems for flying, driving, and shape changing are onboard the robot so that it operates untethered and wirelessly through remote control. Our approach which is simultaneously reversible, repeatable, rapid, untethered, bistable, and low power with powerless shape fixing provides a unique combination of properties for robotic morphing, opening new possibilities for multifunctional, field reconfigurable robots.

[IMAGE OMITTED. SEE PDF]

Results and Discussion

Bistable LaMSA Morphing System

The shape change of the multifunctional robot is performed by a LaMSA system. While LaMSA and other impulsive systems have shown amazing capabilities for robotic jumping,^[33,42] we harness this extraordinary power amplification mechanism to controllably, reversibly, and with low power, rapidly reconfigure the robot. The LaMSA morphing system consists of a central frame with two outer wing links, and is driven by shape memory alloy (SMA) artificial muscle wire and linear springs (Figure 2a,b). The central frame rests in the body of the robot and remains fixed during a shape change. The two smaller, outer wing links move throughout the shape change and are used to mount the propulsion system. This entire morphing system weighs only 51 g, with the rigid frame and links fabricated through additive manufacturing. In either of its stable positions, the linear springs apply a force against mechanical stops at +45° and −45° to maintain the overall shape, such that no power is required for the maintenance of its shape. Additionally, a sufficiently stiff spring is selected so that thrust can be applied without altering the system's position and to prevent inadvertent morphing.

[IMAGE OMITTED. SEE PDF]

To begin a shape change, two of the four SMA wires are activated through the application of electric current. Each of those two SMA wires act on one “wing” of the robot, while the other two SMA wires are used similarly for a return to the previous shape. SMA has a temperature-dependent metallic phase change, which causes the wires to shorten through Joule heating with the applied electric current.^[45] Small relative displacement and a necessary thermal cycling present unique design challenges for integration of SMA into actuation systems;^[46,47] however, SMA allows for compact actuation systems and has a compelling energy density at small volumes and mass.^[48–51] With its use in this bistable LaMSA morphing system, we transform the relatively small stroke of the SMA into a much larger displacement. Additionally, the design of our morphing robot accommodates the necessary thermal cycling by not requiring power to maintain either functional shape. Therefore, through the use of the LaMSA mechanism, we leverage the strengths of SMA while avoiding its drawbacks.

While the SMA wires are initiating the shape change, they simultaneously store energy in the springs; the SMA actuation moves the wing links toward an unstable neutral point where the latch of the LaMSA mechanism “disengages” and the stored spring energy causes a fast movement through the remainder of the motion. The point at which the force of the springs switch from acting counter to the motion to powering the motion is referred to as a torque reversal latch,^[32] as the direction of the moment applied about the axis of rotation is reversed across the unstable neutral point. This rapid actuation sequence is captured with a high speed camera and displayed in Figure 2c and Video S1, Supporting Information. The labels indicate which of the SMA or spring is applying the force powering movement, and the torque reversal latch is at the midpoint of the motion where the outer wing link is horizontal.

The resultant motion of this spring actuated system is rapid, as shown in Figure 2d. A 50 ms inset displays the rapid motion which is characteristic of a majority of the stroke. We find that the whole shape change motion takes ≈120 ms for a single wing when considering the initial build-up of SMA actuation. The latter 45° of the stroke, after the latch is released, can take as little as 5 ms. This shows that a LaMSA system can provide very rapid morphing, exceeding that provided by servo motors or other methods at similar scales (Figure S1, Supporting Information). This rapid morphing capability can enable a fast and power efficient response in crucial, time-sensitive situations, such as search and rescue. Additionally, power consumption for this test is measured in the range of 8–8.5 W for the short duration of the shape change, so the energy input required by the LaMSA system is approximately 11.5 J. To show the reversibility and repeatability of the shape change mechanism, 100 cycles of the shape change are performed sequentially with an interval of 8 s; this time step is chosen to accommodate the thermal cycle of the SMA. During the test, there is no loss in shape change performance (Figure 2e), and we can load and release the latch many times without external intervention.

For the bistable LaMSA morphing system within the multifunctional robot, the added weight of the motors and propeller/wheels on the wing links does not impede the morphing operation. A small difference in the time to change is observed and reported in Figure S2, Supporting Information, wherein the driving-to-flying shape change requires slightly more time for the SMA wire actuator to heat and begin the motion, and the flying-to-driving slightly less; however, the time for the motion itself is similar at approximately 120 ms. With this rapid, reversible, repeatable, and reliable shape change, the multifunctional robot can switch between its flying and driving states on demand, and stay in either shape without any power consumption.

Locomotion Methodology and Performance

The multifunctional robot uses the same motors for both flying and driving modes of transport. Through the use of a shared propulsion system, we can reduce the robot's complexity and redundancy compared to robots which achieve multi-modality through the use of disparate systems on the same frame,^{[10–12,16,21,24,52–56]} or shared propulsion systems that require the addition of secondary or passive rolling hardware.^[20,57–59] We utilize a specifically-designed combination of a propeller and wheel as a single structure that performs both modes of transport using the same motors, without adding extensive material or complexity to the robot. It consists of a polycarbonate rim which is adhered at each blade tip to a 5-blade, bullnose propeller (Figure 3a). A bullnose propeller design is selected so that the impact on thrust-dependent aerodynamics with the addition of a wheel rim is theoretically minimized; this is because we do not need to modify the blade tips to achieve a strong connection to the outer rim, and the aerodynamics of the original propellers can be preserved.

[IMAGE OMITTED. SEE PDF]

When the multifunctional robot is in its driving shape, the propeller/wheel structures rotate along the ground for driving. To examine the efficiency of locomotion, we measured the cost of transport (COT), which is given by P_in/mgv, where P_in is the power applied, m is the mass of the robot (450 g), g is acceleration due to gravity, and v is its velocity.^[17] From this, we observe that 3% motor output gives the greatest efficiency with a cost of transport of 0.43, during which the robot travels at 5.13 m s⁻¹, or 22.3 body lengths/s (Figure 3b). We found the fastest controlled ground speed to be 9.71 m s⁻¹, or 42.2 body lengths s⁻¹ at 10% motor output (during our testing, motor outputs greater than 10% were not feasible while driving due to loss of traction). The cost of transport for this speed is still a low 0.99, and is a significant improvement over the flight cost of transport of 14.0, while achieving similar velocities. Overall, this shows excellent velocity and COT, exceeding that of other quadcopter or bio-inspired multi-modal robots, and showing similar or better performance compared to multiple specialized, single mode robots (Figure S3, Supporting Information).^{[10,16,17,52,54,58,60–66]}

When the multifunctional robot is in its flying shape, the propeller/wheel structure is oriented such that the propeller blades generate vertical thrust upon activation of the motors. When measuring the flying thrust, the multifunctional propeller/wheel performs within 10% of the stock propeller at relevant motor outputs, providing sufficient lift for flight as a quadcopter drone while also enabling ground transportation (Figure 3c). Power consumption is kept constant between the two cases.

Images from the driving component of this dual locomotion capability are presented in Figure 3d, where the multifunctional robot travels across a tennis court in 3.4 s from a standstill. This is done using 3% motor output, and the velocity and power over time are shown in Figure 3e. The switch to driving mode requires a control adjustment, which is achieved through applying an auxiliary circuit to the quadcopter drone control board, a diagram and additional details of which is provided in Figure S4, Supporting Information. This supplementary control system of the multifunctional robot, which also provides electrical power delivery for the shape change, is completely integrated onboard, and all functions are wirelessly controlled.

We show the flying portion of the dual locomotion capability in Figure 3f. Additionally, we measure the top air speed of the robot at 9.17 m s⁻¹ and its cost of transport at 14.0. The corresponding video for both driving (Figure 3d) and flying (Figure 3f) is presented in Video S2, Supporting Information. With this, we are able to successfully utilize the same motors for both modes of transport, reducing the necessary components, controls, and complexity required to achieve flying and driving multifunctionality.

Multifunctional Flying and Driving Robot

With the shape change, dual-mode locomotion system, and integrated onboard electronics, the multifunctional flying and driving robot is wirelessly controlled such that it is able to drive, fly, and change its shape as necessary. These capabilities are demonstrated by navigating the robot through an obstacle course that would not be readily navigable by a flying or driving robot alone. This obstacle course is modeled after a destroyed building to emulate use in search and rescue, in which irregular and confined spaces may be present. In Figure 4a, the path of the robot through the obstacle course is shown, with the driving portions marked in blue and the flying portions in green. The driving portion includes a narrow tunnel, after which a wall restricts further ground movement, requiring a morphing transition and then the use of its flying function. After landing, the robot morphs again and drives away. During its endeavor, the robot changes shape twice to continue on the designed path. This sequence is also presented in Video S3, Supporting Information.

[IMAGE OMITTED. SEE PDF]

In Figure 4b, the sequence of the robot transforming from its driving shape to flying shape is shown. The force exerted by the SMA wire on the wings is enough to overcome the initial friction between the rims and the ground. The ability to switch to flying is useful for overcoming large obstacles, reaching elevated regions, or bypassing unnavigable terrain, such as the low wall in the demonstration course. Figure 4c shows the flying-to-driving shape change of the robot. The ability to switch to driving is useful for travelling through confined spaces (such as the narrow tunnel in the demonstration course) and traversing long distances with less power than flying. During this change, the spring actuation is powerful enough to lift the entire body of the robot off the ground, giving it the clearance it needs to drive. This is enabled by the power amplification of the LaMSA system, as the power output of the springs exceeds that which can be achieved through the SMA wire actuators at this scale. This enables us to achieve unique capabilities, compactness, and efficiency compared to previous examples of multimodal or morphing robotic systems (Figure S5, Supporting Information). The ability to change shape allows the robot to adapt better to its surroundings, giving it the ability to travel over more varied terrain than a single locomotion mode. All of these functions are performed by remote control, and it does not require any manual manipulation to fly, drive, and morph as shown.

Conclusion

We show how using a LaMSA system in a multifunctional robot represents an opportunity to utilize these rapid, bio-inspired movements as components in overall robotic design. Prior works have asserted that LaMSA systems are only competitively useful at miniature scales, as traditional actuation strategies such as motors provide better power per weight or volume for larger devices.^[32,67] While LaMSA and other impulsive systems have shown amazing capabilities for robotic jumping,^[33,42–44] we harness this extraordinary power amplification mechanism to controllably, reversibly, and with low power, rapidly reconfigure a multifunctional robot for the first time. With this, we demonstrate how integration of small-scale LaMSA systems into larger devices can be a powerful enabler to improve functionality and performance. Our LaMSA morphing system combines reversibility, repeatability, rapidity, bistability, and low power with powerless shape fixing, a critical combination for a future where robots can be reconfigured in the field to perform multiple different tasks. By enabling multiple modes of transportation with this morphing technique, we demonstrate a multifunctional robot which can travel in complex environments, as well as operate in an untethered and remote manner. These capabilities can facilitate robotic vehicles to travel across diverse terrain: a single robot could traverse a tight space at elevation by flying then driving, or a robot could traverse long distances by driving while still retaining energy to complete a flight mission, all while the operator remains a safe distance away. Additionally, the quick response offered by LaMSA mechanisms presents future opportunities to rapidly morph robots during flight for enhanced control or for morphing between locomotion modes while still in flight to enable landing in driving configurations for seamless transitions. By considering LaMSA systems as enablers of multifunctionality or enhancements of existing systems rather than exclusively designing robots around LaMSA as the driver of a robot's primary functionality, new opportunities in advanced robots can be opened in which LaMSA principles can be applied to a greater range of robotic designs, sizes, and functionality.

Experimental Section

Fabrication of Rigid Components

Custom rigid components for the multifunctional robot were fabricated through fused filament additive manufacturing. A Raise3D Pro2 with polylactic acid (PLA) filament (Raise3D Premium PLA) and polyvinyl alcohol (PVA) dissolvable support filament (Raise3D Premium PVA+) was used to create custom components. The PVA supports were dissolved in warm tap water.

Assembly of Bistable LaMSA Morphing System

The rigid links of the bistable LaMSA morphing system are connected at the hinges with M3 bolts, with small 3D-printed pulleys placed in each gap for the SMA wires (Flexinol MuscleWire, 250 μm diameter). Flexinol Self-Crimps are affixed to the SMA wire at a distance of 200 mm apart, and are mounted to the linkage using M2 bolts and nuts, which can be adjusted to the exact required length. The end loops of the springs (McMaster part number 5108N123) are connected to M3 bolts in the locations shown in Figure 2a.

Fabrication of Customized Propellers

A circle of inner diameter 75mm and outer diameter 80 mm was cut from a sheet of polycarbonate (McMaster part number 87225K21) with a Universal Laser Systems VLS4.75 laser cutter. This was adhered to the propeller (HQprop Duct-T75MMX5) at the edges using a cyanoacrylate adhesive (Loctite 406).

High Speed Actuation Measurement

Movement of the bistable morphing system was measured through high speed video recording and motion tracking software. We used a Chronos CR21-HD high speed camera to record the bistable LaMSA morphing system at up to 1000 frames s⁻¹. The actuation was triggered by a Keithley 2461 Sourcemeter, set to apply 1.05 A for 1.3 s. An LED is set up in the same circuit as the morphing system and placed within the shot of the high speed camera such that the duration for which current is applied is apparent in the video. The angle of the wing linkage is measured using the video motion tracking software Physlets Tracker. For the many successive shape changes data, the sourcemeter is set up to automatically repeat the pulse every 8 s for 100 times, and a double-pole, double-throw mechanical switch is added to control which pair of SMA wires is powered. This switch is flipped by hand between every instance of power application.

Propeller Thrust Measurement

The thrust of the propeller, as shown in Figure 3c, was measured using an Instron 5944 universal testing system. The test was performed with a 50 N load cell. A custom-made 3D-printed fixture attached a motor and propeller to the load cell, so that thrust generated would cause a pulling force on the load cell. The motor was controlled using the motor testing tool of Betaflight Configurator to control the motor output amount. This testing setup is shown in Figure S6, Supporting Information.

Speed Measurement

The speed of the drone for both driving and flying was measured with a GPS speed meter (SkyRC GSM-015). For driving, the tests were conducted across a set of tennis courts. For flying, the tests were conducted at the Virginia Tech Drone Park (https://ictas.vt.edu/Facilities/ictas-drone-park.html). The power consumption data was collected using the blackbox feature available on the flight controller with Betaflight and Betaflight Blackbox Configurator with a conversion factor shown in Figure S7, Supporting Information.

Acknowledgements

B.T.W., J.J., and M.D.B. acknowledge support from the Office of Naval Research Young Investigator Program (ONR YIP) (N000142112699) and the Defense Advanced Research Projects Agency Young Faculty Award (DARPA YFA) (D18AP00041).

Conflict of Interest

M.D.B. and B.T.W. are inventors on a patent application (US Patent Application No: 63/390,027) on the multifunctional robot design. The remaining authors declare no competing interests.

Author Contributions

B.T.W. and M.D.B. conceived the idea. B.T.W. designed systems and components with input from J.J and M.D.B. B.T.W. and J.J. fabricated components, performed experiments, and analyzed results. B.T.W. and M.D.B. wrote the paper with input from J.J., and M.D.B. supervised the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

A. Jusufi, D. I. Goldman, S. Revzen, R. J. Full, Proc. Natl. Acad. Sci. U S A 2008, 105, 4215.

J. A. Nirody, J. Jinn, T. Libby, T. J. Lee, A. Jusufi, D. L. Hu, R. J. Full, Curr. Biol. 2018, 28, 4046.

R. J. Lock, S. C. Burgess, R. Vaidyanathan, Bioinspiration Biomimetics 2014, 9, 011001.

D. Floreano, R. J. Wood, Nature 2015, 521, 460.

D. K. Patel, X. Huang, Y. Luo, M. Mungekar, M. K. Jawed, L. Yao, C. Majidi, Adv. Mater. Technol. 2023, 8, 2201259.

H. Bai, R. F. Shepherd, Curr. Opin. Solid State Mater. Sci. 2021, 25, 100894.

P. H. Nguyen, M. Kovač, B. C. Arrue, Front. Robot. AI 2023, 10.

L. Li, S. Wang, Y. Zhang, S. Song, C. Wang, S. Tan, W. Zhao, G. Wang, W. Sun, F. Yang, J. Liu, B. Chen, H. Xu, P. Nguyen, M. Kovac, L. Wen, Sci. Robot. 2022, 7, eabm6695.

S. Hamaza, M. Kovac, presented at 2020 17th Int. Conf. on Ubiquitous Robots (UR), IEEE, pp. 153–158.

R. J. Bachmann, F. J. Boria, R. Vaidyanathan, P. G. Ifju, R. D. Quinn, Mech. Mach. Theory 2009, 44, 513.

K. Peterson, P. Birkmeyer, R. Dudley, R. S. Fearing, Bioinspiration Biomimetics 2011, 6, 046008.

J. Hu, Y. Liang, X. Diao, in IEEE Int. Conf. on Robotics and Biomimetics (ROBIO), IEEE, Piscataway, NJ 2017, pp. 270–275.

S. Kim, Y.‐H. Hsiao, Y. Chen, J. Mao, Y. Chen, IEEE Robot. Autom. Lett. 2022, 7, 6950.

W. R. T. Roderick, M. R. Cutkosky, D. Lentink, Sci. Robot. 2021, 6, eabj7562.

S. Tanaka, A. Asignacion, T. Nakata, S. Suzuki, H. Liu, Drones 2022, 6, 1.

L. Daler, S. Mintchev, C. Stefanini, D. Floreano, Bioinspiration Biomimetics 2015, 10, 016005.

R. Baines, S. K. Patiballa, J. Booth, L. Ramirez, T. Sipple, A. Garcia, F. Fish, R. Kramer‐Bottiglio, Nature 2022, 610, 283.

D. Shah, B. Yang, S. Kriegman, M. Levin, J. Bongard, R. Kramer‐Bottiglio, Adv. Mater. 2021, 33, 1.

A. Pratt, P. Wilcox, C. Hanson, R. F. Shepherd, IEEE Robot. Autom. Mag. 2023, 30, 20.

N. Meiri, D. Zarrouk, in Int. Conf. on Robotics and Automation (ICRA), IEEE 2019, pp. 5302–5308.

S. Mintchev, D. Floreano, presented at 2nd Int. Symp. on Aerial Robotics, IEEE.

T. Tuna, S. Ertug Ovur, E. Gokbel, T. Kumbasar, Aerosp. Sci. Technol. 2020, 100, 105807.

X. Li, J. McWilliams, M. Li, C. Sung, C. Jiang, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2021, IEEE, Piscataway, NJ 2021, pp. 7107–7113.

H. Jia, S. Bai, R. Ding, J. Shu, Y. Deng, B. L. Khoo, P. Chirarattananon, IEEE/ASME Trans. Mechatron. 2022, 27, 4741.

N. Bucki, J. Tang, M. W. Mueller, IEEE Trans. Robot. 2023, 39, 539.

E. Sihite, A. Kalantari, R. Nemovi, A. Ramezani, M. Gharib, Nat. Commun. 2023, 14, 3323.

D. Falanga, K. Kleber, S. Mintchev, D. Floreano, D. Scaramuzza, IEEE Robot. Autom. Lett. 2019, 4, 209.

P. M. Kornatowski, M. Feroskhan, W. J. Stewart, D. Floreano, IEEE Robot. Autom. Lett. 2020, 5, 4233.

D. Hwang, E. J. Barron, A. B. Haque, M. D. Bartlett, Sci. Robot. 2022, 7, eabg2171.

A. Tonazzini, S. Mintchev, B. Schubert, B. Mazzolai, J. Shintake, D. Floreano, Adv. Mater. 2016, 28, 10142.

S. J. Longo, S. M. Cox, E. Azizi, M. Ilton, J. P. Olberding, R. St Pierre, S. N. Patek, J. Exp. Biol. 2019, 222, jeb197889.

M. Ilton, M. S. Bhamla, X. Ma, S. M. Cox, L. L. Fitchett, Y. Kim, J.‐S. Koh, D. Krishnamurthy, C.‐Y. Kuo, F. Z. Temel, A. J. Crosby, M. Prakash, G. P. Sutton, R. J. Wood, E. Azizi, S. Bergbreiter, S. N. Patek, Science 2018, 360, eaao1082.

E. W. Hawkes, C. Xiao, R.‐A. Peloquin, C. Keeley, M. R. Begley, M. T. Pope, G. Niemeyer, Nature 2022, 604, 657.

S. Divi, X. Ma, M. Ilton, R. St. Pierre, B. Eslami, S. N. Patek, S. Bergbreiter, J. R. Soc. Interface 2020, 17, 20200070.

T. J. Roberts, E. Azizi, J. Exp. Biol. 2011, 214, 353.

E. Steinhardt, N.‐S. P. Hyun, J.‐S. Koh, G. Freeburn, M. H. Rosen, F. Z. Temel, S. N. Patek, R. J. Wood, Proc. Natl. Acad. Sci. U S A 2021, 118, e2026833118.

S. N. Patek, J. E. Baio, B. L. Fisher, A. V. Suarez, Proc. Natl. Acad. Sci. U S A 2006, 103, 12787.

Z. Zhakypov, K. Mori, K. Hosoda, J. Paik, Nature 2019, 571, 381.

Y. Forterre, J. M. Skotheim, J. Dumals, L. Mahadevan, Nature 2005, 433, 421.

S.‐W. Kim, J.‐S. Koh, J.‐G. Lee, J. Ryu, M. Cho, K.‐J. Cho, Bioinspiration Biomimetics 2014, 9, 036004.

Y. Chi, Y. Li, Y. Zhao, Y. Hong, Y. Tang, J. Yin, Adv. Mater. 2022, 34, 2110384.

Y. Wang, Q. Wang, M. Liu, Y. Qin, L. Cheng, O. Bolmin, M. Alleyne, A. Wissa, R. H. Baughman, D. Vella, S. Tawfick, Proc. Natl. Acad. Sci. U S A 2023, 120, 2017.

S. Divi, R. St. Pierre, H. M. Foong, S. Bergbreiter, Bioinspiration Biomimetics 2023, 18, 066003.

M. Kovac, M. Fuchs, A. Guignard, J.‐C. Zufferey, D. Floreano, in IEEE Int. Conf. on Robotics and Automation, IEEE, Piscataway, NJ 2008, pp. 373–378.

J. Mohd Jani, M. Leary, A. Subic, M. A. Gibson, Mater. Des. 2014, 56, 1078.

T. Schmelter, B. Theren, S. Fuchs, B. Kuhlenkőtter, Crystals 2021, 11, 163.

G. Song, N. Ma, H.‐J. Lee, S. Arnold, Smart Mater. Struct. 2007, 16, 1342.

A. Nespoli, S. Besseghini, S. Pittaccio, E. Villa, S. Viscuso, Sens. Actuators A 2010, 158, 149.

E. A. Peraza‐Hernandez, D. J. Hartl, R. J. Malak Jr., D. C. Lagoudas, Smart Mater. Struct. 2014, 23, 094001.

J. K. Paik, E. Hawkes, R. J. Wood, Smart Mater. Struct. 2010, 19, 125014.

Z. Zhakypov, J.‐L. Huang, J. Paik, IEEE Robot. Autom. Mag. 2016, 23, 65.

Y. Mulgaonkar, B. Araki, J.‐S. Koh, L. Guerrero‐Bonilla, D. M. Aukes, A. Makineni, M. T. Tolley, D. Rus, R. J. Wood, V. Kumar, in IEEE Int. Conf. Robot. Autom., IEEE, Piscataway, NJ 2016, pp. 4672–4679.

M. Pitonyak, F. Sahin, in IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), IEEE, Piscataway, NJ 2017, pp. 1447–1452.

K. Kim, P. Spieler, E. S. Lupu, A. Ramezani, S. J. Chung, Science Robotics 2021, 6, eabf8136.

S. Atay, T. Jenkins, G. Buckner, M. Bryant, Int. J. Intell. Robot. Appl. 2020, 4, 3.

H. Jia, R. Ding, K. Dong, S. Bai, P. Chirarattananon, IEEE Robot. Autom. Lett. 2023, 8, 4052.

J. R. Page, P. E. I. Pounds, in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2014 IEEE, Piscataway, NJ, pp. 4834–4841.

A. Kalantari, M. Spenko, IEEE Trans. Robot. 2014, 30, 1278.

S. Jeong, S. Jung, Mechatronics 2014, 24, 1178.

G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, S. Kim, in IEEE Int. Conf. on Intelligent Robots and Systems, IEEE, Piscataway, NJ 2018, pp. 2245–2252.

U. Saranli, M. Buehler, D. E. Koditschek, Int. J. Robot. Res. 2001, 20, 616.

S. Kitano, S. Hirose, A. Horigome, G. Endo, ROBOMECH J. 2016, 3, 8.

M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, M. Hoepflinger, in IEEE Int. Conf. on Intelligent Robots and Systems, IEEE, Piscataway, NJ 2016, pp. 38–44.

P. A. Bhounsule, J. Cortell, A. Grewal, B. Hendriksen, J. G. D. Karssen, C. Paul, A. Ruina, Int. J. Robot. Res. 2014, 33, 1305.

A. Spröwitz, A. Tuleu, M. Vespignani, M. Ajallooeian, E. Badri, A. J. Ijspeert, Int. J. Robot. Res. 2013, 32, 932.

J.‐A. Meyer, H. L. Roitblat, S. W. Wilson, in From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior, The MIT Press, Cambridge, MA 1993.

G. P. Sutton, E. Mendoza, E. Azizi, S. J. Longo, J. P. Olberding, M. Ilton, S. N. Patek, Integr. Comp. Biol. 2019, 59, 1609.