Artificial control of animal locomotion has the potential to simultaneously address longstanding challenges to actuation, control, and power requirements in soft robotics. Robotic manipulation of locomotion can also address previously inaccessible questions about organismal biology otherwise limited to observations of naturally occurring behaviors. Here, we present a biohybrid robot that uses onboard microelectronics to induce swimming in live jellyfish. Measurements demonstrate that propulsion can be substantially enhanced by driving body contractions at an optimal frequency range faster than natural behavior. Swimming speed can be enhanced nearly threefold, with only a twofold increase in metabolic expenditure of the animal and 10 mW of external power input to the microelectronics. Thus, this biohybrid robot uses 10 to 1000 times less external power per mass than other aquatic robots reported in literature. This capability can expand the performance envelope of biohybrid robots relative to natural animals for applications such as ocean monitoring.

Previous electrophysiology studies have shown that applying a periodic electric current can incite rhythmic muscle contractions in constrained medusae, but the specific input-output responses—from electrical stimulation to muscle contraction—have not been fully described ( 27 – 31 ). We systematically characterized the spatiotemporal response of Aurelia to electrical signals of varying amplitudes (A), pulse durations (T), frequencies (f), and electrode locations to identify combinations that robustly and repeatably incite muscle contractions (see Supplementary Text and fig. S1 for further discussion). This characterization guided the design of a microelectronic system for external control of jellyfish swimming.

( A ) Square wave signal generated by the swim controller with an amplitude (A) of 3.7 V and a pulse width (T) of 10 ms, set at frequencies (f) of 0.25, 0.38, 0.50, 0.62, 0.75, 0.88, and 1.00 Hz. ( B ) Swim controller components. Housing includes (i) a polypropylene cap with a wooden pin that embeds into the bell center, and (ii) a plastic film to waterproof the housing, both offset with stainless steel and cork weights to keep the device approximately neutrally buoyant. Microelectronics include (iii) a TinyLily mini-processor, (iv) lithium polymer battery, and (v) two platinum-tip electrodes with LEDs to visually indicate stimulation. ( C ) Fully assembled device, with the processor and battery encased in the housing. ( D ) Simplified schematics of A. aurita anatomy, highlighting the subumbrellar (top) and exumbrellar (bottom) surfaces, rhopalia, muscle ring, and circumferential muscle fiber orientation, oral arms, and gonads/gastric pouches. ( E ) Swim controller (inactive) embedded into a free-swimming jellyfish, bell oriented subumbrellar side up, with the wooden pin inserted into the manubrium and two electrodes embedded into the muscle and mesogleal tissue near the bell margin. Photo credits for (B), (C), and (E): Nicole W. Xu, Stanford University.

Figure 1 summarizes our scheme for controlled swimming in jellyfish. Aurelia aurita is an oblate species of jellyfish comprising a flexible mesogleal bell and monolayer of coronal and radial muscles that line the subumbrellar surface (see schematic in Fig. 1D ). To swim, the muscles contract to decrease the subumbrellar cavity volume and eject water to provide motive force ( 21 , 22 , 24 , 25 ), with additional contributions to forward motion from passive energy recapture ( 1 ) and suction-based propulsion ( 26 ). To initiate these muscle contractions, the animal activates any of its eight pacemakers, located within rhopalia (sensory organs) along the bell margin. These nerve clusters activate the entire motor nerve net and cause bidirectional muscle wave propagations originating from the activated pacemakers ( 21 , 22 , 24 , 25 , 27 – 31 ).

To illustrate the power of this new approach for basic science, we hypothesized that increasing bell contraction frequencies increases swimming speeds up to a limit, in which the kinematics are compromised when the bell cannot fully relax before the next pulse, but at an energetic cost that follows a cubic power law. By externally controlling the frequency of pulses in free-swimming animals and by measuring the corresponding swimming speed and oxygen consumption, we calculated the COT to test the aforementioned hypothesis in this work. Such an examination was previously only possible in theoretical or computational models.

Jellyfish swimming also provides a source of inspiration for studying basic science questions regarding animal-fluid interactions ( 19 , 20 ). Because locomotion is required for jellyfish to feed, escape predators, and reproduce ( 21 , 22 ), their biomechanics and ecology are intimately connected, with implications for phenomena such as jellyfish blooms ( 23 ). However, current studies of jellyfish are limited to observations of endogenous swimming. User control of swimming could enable a much broader range of studies of the biology and ecology of animal locomotion in laboratory and in situ experiments.

Because jellyfish are naturally found in a wide range of salinities, temperatures, oxygen concentrations, and depths (including 3700 m or deeper in the Mariana Trench) ( 14 , 15 ), these biohybrid robots also have the potential to be deployed throughout the world’s oceans. Because biologging larger marine animals has been shown to expand the capabilities of ocean observations ( 16 ), the user control of jellyfish could further expand ocean monitoring and robotic sampling as an additional resource to current work using autonomous underwater vehicles (AUVs) ( 17 ) and hydroacoustics ( 18 ).

By using live jellyfish as a natural scaffold, we can use the animals’ own basal metabolism to reduce power requirements, leverage its muscles for actuation, and rely on self-healing and regenerative tissue properties for increased damage tolerance. Although more work is needed to improve the maneuverability of robots that use live animals, in this work, we have constructed a biohybrid robot that is 10 to 1000 times more energy efficient than existing swimming robots reported in literature, by integrating microelectronics in live jellyfish.

Actuation and power consumption remain two primary limitations of robotic systems. Yang et al. ( 8 ) highlight biohybrid and bioinspired soft robots as a means to improve robotics, using biological organisms as a gold standard of performance. Potential advances include batteries that match low metabolic energy expenditures in animals, muscle-like actuators, and self-healing and self-manufacturing materials ( 8 ). Currently, mechanical soft robots that mimic fish and jellyfish propulsion leverage engineered materials. However, these biomimetic robots exhibit higher energy consumption than their animal counterparts and are therefore typically tethered to external power supplies ( 3 , 7 ). In contrast, biological soft robots require less power. Examples of these bottom-up approaches include artificial jellyfish and rays made from rat cardiomyocytes seeded on silicon scaffolds ( 9 , 10 ), as well as robots that incorporate skeletal muscle, collagen, and sea slug tissue cultures for additional features, such as speed and controllability ( 11 – 13 ). However, such biological robots are limited to swimming in cell medium cultures for survival.

Jellyfish are compelling model organisms for more energy-efficient underwater vehicles because of their low cost of transport (COT; or mass-specific energy input per distance traveled) ( 1 ). Existing robotic mimics of swimming animals composed entirely of engineered components can achieve velocities comparable to natural animals, but with orders of magnitude less efficiency than jellyfish ( 2 – 7 ). In contrast, biohybrid jellyfish robots that incorporate live animals offer potential advantages that address the grand challenges of robotics ( 8 ), by using the jellyfish structure and muscle for actuation, solving the power requirements by leveraging natural feeding behaviors to extract chemical energy from prey in situ, and recovering from damage via wound healing processes that are inherent to the animal. This robotic approach to controlling animal locomotion can also enable further studies of live organism biomechanics in user-controlled experiments. Thus, a biohybrid robot that uses a system of microelectronics to externally control swimming in live jellyfish can advance both the science and engineering of aquatic locomotion.

RESULTS

Robotic design and implementation in live jellyfish On the basis of extensive characterization of the spatiotemporal parameter space of jellyfish muscle stimulation (see Supplementary Text and fig. S1), we created a portable, self-contained microelectronic swim controller that generates a square pulse wave (A = 3.7 V, T = 10 ms; Fig. 1A) to stimulate muscle contractions from 0.25 to 1.00 Hz. As shown in Fig. 1 (B and C), the controller is composed of a TinyLily mini-processor (TinyCircuits, Akron, OH, USA) and a 10-mAh lithium polymer cell (GM201212, PowerStream Technology Inc., Orem, UT, USA) encased in a 2.11-cm-diameter cylindrical polypropylene housing and sealed with Parafilm M Film (Bemis Company Inc., Oshkosh, WI, USA). Two wire electrodes were composed of perfluoroalkoxy (PFA)–coated silver wire with a bare diameter of 76.2 μm and a coated diameter of 139.7 μm, and platinum rod tips with a diameter of 254.0 μm (A-M Systems, Sequim, WA, USA). The wires were connected in series to TinyLily 10402 light-emitting diodes (LEDs; TinyCircuits, Akron, OH, USA) for visual confirmation of the electrical signal. To attach the swim controller to the jellyfish, a 2.5-cm wooden pin attached to the center of the polypropylene housing was inserted into the manubrium of the bell, on the subumbrellar side of the bell center. Electrodes were inserted bilaterally into the subumbrellar tissue midway between the bell margin and center (Fig. 1E, jellyfish oriented subumbrellar surface upward). The system weight was offset with stainless steel washers and cork to keep the system approximately neutrally buoyant.

Device validation To validate that the swim controller can externally drive jellyfish bell contractions, we developed a method to track motion of the bell margin. A. aurita medusae were placed subumbrellar surface up in a plate without seawater, with tags injected into the tissue (red dots, circled in Fig. 2A; see Materials and Methods). From tag displacements, such as the example curves shown in Fig. 2 (B to D), we calculated the single-sided amplitude spectrum (SSAS) to obtain the mean peak and mean full width at half maximum (FWHM) of bell contractions over time. Fig. 2 Signal validation using visual tags and frequency spectra to track muscle contractions. (A) A. aurita medusae (n = 10, 8.0 to 10.0 cm in diameter) were placed subumbrellar surface up in a plate without seawater for constrained muscle stimulation experiments (electrode not shown). The image is inverted so that the bell and plate are white, and black areas are reflections of light from animal tissue and the plate. For clarity, the margin of the bell is outlined in a red dotted circle, and the oral arms are colorized in blue. Visible implant elastomer tags (shown as colored red dots within red circles) were injected around the margin, and one tag was tracked per video to calculate the tissue displacement as a surrogate for muscle contractions. Spatial tests to determine whether electrode location affected the spectra were conducted at four locations, labeled in red numbers: (1) adjacent to the gastric pouches, (2) midway between the gastric pouches and margin, (3) at the rhopalia, and (4) at the margin away from the rhopalia (see “Extended results” sections in Supplementary Text). All other tests were conducted at location 2. (B) Example tag displacement as a function of time for an animal without any external stimulus. The red line indicates the centroid displacement, with the error calculated from assuming a half-pixel uncertainty in finding the centroid of the tag in each image, over 25 s. Note the temporal variation of muscle contractions, including periods of regular pulses and successive rapid pulses. (C) Example tag displacement for an animal with an external stimulus of 0.25 Hz, with each stimulus visualized as a vertical black line. Although contractions regularly follow external stimuli, natural animal pulses also occur at low frequencies. Note, for example, the double pulse after one stimulus (t ≈ 12 s). (D) Example tag displacement for an animal with an external stimulus of 1.00 Hz, with each stimulus visualized as a vertical black line. The same time window (25 s) is shown for a fair comparison to the previous two plots. Contractions regularly follow external stimuli. (E) SSASs averaged for jellyfish without any external stimulus (n = 12 for 10 animals, i.e., 2 jellyfish had two replicate clips each). The red line indicates the mean of normalized SSAS for each replicate, with the SD in pink. The peak of the mean SSAS is at 0.16 Hz. The FWHM is 0.24 Hz. (F) Jellyfish response to an inactive electrode embedded (n = 14 for 10 animals, i.e., 4 jellyfish had two replicate clips each). The peak of the mean SSAS is at 0.18 Hz. The FWHM is 0.16 Hz. Using a two-sample t test of the peak frequencies for both groups, the difference between the two samples was statistically insignificant (P = 0.68). (G) Sample SSAS for an electrical stimulus at 1.00 Hz (n = 10 jellyfish for an input signal of 4.2 V and 4.0 ms). The peak frequency occurs at 1.02 Hz, within the 0.02 window used to calculate the SSAS. Note that the spectrum has a sharper peak at the frequency of interest (FWHM of 0.04 Hz), as opposed to a wider FWHM in (B) and (C), the cases without any external stimulus. (H) Contour map of the frequency response of muscle contractions to external electrical stimuli. Each vertical line of data (centered on white lines at 0.25, 0.50, 0.75, 1.00, 1.20, 1.50, and 2.00 Hz) represents the PSD at one electrical input frequency, with the number of jellyfish tested shown above. The colors correspond to the amplitude of the PSD, in which higher values are shown in yellow and lower values in blue. The solid red line represents a one-to-one input-output response, and the dashed red line represents the reported physiological limit according to the minimum absolute refractory period of A. aurita muscle (32). Responsive trials are defined by whether the peak frequencies in the PSD lie within a window of 0.06 Hz of the solid red curve. (I) Contour maps of the unresponsive trials. Higher frequencies up to 90.00 Hz were also tested with similar unresponsive PSDs. Photo credit for (A): Nicole W. Xu, Stanford University. Three sets of experiments were conducted: controls to directly observe the animals’ endogenous contractions in the absence of any perturbations, controls to observe whether mechanically embedding inactive electrodes would affect natural animal behavior, and stimulation protocols to confirm externally driven contractions. Endogenous contractions (natural animal behavior). Natural contractions were irregular with high pulse rate variability (Fig. 2B). Measurements indicated a mean peak frequency value of 0.16 Hz and an FWHM of 0.24 Hz for n = 12 (10 animals, with two replicates each for 2 of the animals), as shown in Fig. 2E. The FWHM reflected natural inter-animal and intra-animal variation of endogenous swimming. An inactive electrode was tested to determine whether insertion affected the bell contraction frequency. This resulted in a mean peak frequency value of 0.18 Hz and an FWHM of 0.16 Hz, as shown in the mean SSAS in Fig. 2F, for n = 14 (10 animals, with two replicates each for 4 of the animals). The difference between the two mean peak values was statistically insignificant using a two-sample t test (P = 0.68). This suggests that the frequency spectra are not significantly changed by mechanical artifacts from implanting the electrode, i.e., any statistically significant changes to the frequency spectra of externally stimulated animals are due to external electrical stimulation. Externally driven contractions. In comparison to the animals’ endogenous pulses, which occur naturally without external stimuli, externally driven contractions resulted in tag displacement curves shown in Fig. 2C (driven at 0.25 Hz) and Fig. 2D (driven at 1.00 Hz). Black vertical lines indicate each single square pulse stimulus (Fig. 1A), which underscores the regularity of muscle contractions post-stimulus. However, variations do occur, such as the endogenous bell contraction in the 0.25-Hz test (Fig. 2C, t ≈ 12 s). See the “Limitations” section in Supplementary Text for further discussion. For an externally driven frequency input of 1.00 Hz, the normalized SSAS is shown in Fig. 2G, featuring a sharp, narrow peak at the driven frequency. Note that compared to the endogenous SSAS curves in Fig. 2 (E and F), this peak is narrower (i.e., FWHM = 0.04 Hz), reflecting more regular bell contractions. Externally driven contraction frequency map. The frequency response of the animal contractions to external electrical stimulation is plotted as a contour map in Fig. 2 (H and I), in which discrete vertical lines (shown in white) represent power spectral densities (PSDs) for each electrical frequency input. The number of trials corresponding to each vertical PSD column is listed above each data line. The map is colored by the amplitude of the mean normalized PSD (with interpolated values between data lines), from higher amplitudes in yellow to lower amplitudes in blue. The solid red line indicates a one-to-one response, i.e., if a peak occurs within 0.06 Hz of the input frequency. This band is the windowing error based on the resolution of the PSD. The dashed red line indicates the reported physiological limit of jellyfish muscle contractions at 1.4 Hz, according to the observed absolute refractory period of A. aurita muscle (29). We observe that the muscle can respond slightly above this limit (i.e., 1.50 Hz). As plotted, one-to-one input-output responses were observed for each tested stimulation frequency of up to 1.00 Hz (Fig. 2H). From 1.20 to 2.00 Hz, the number of responsive cases decreased until all jellyfish tested did not respond at frequencies above 1.50 Hz. These unresponsive cases showed SSASs and PSDs similar to the unstimulated control cases (Fig. 2I). Higher frequencies above those shown on the contour maps were also tested, with no occurrence of tetany and similar frequency responses to those in the unstimulated control groups. The two-sample t test for SSASs at 10 Hz (n = 8 animals) yielded P = 0.16 and 0.36 compared to the two controls, respectively, and at 90 Hz (n = 9 animals) yielded P = 0.46 and 0.80. At the lowest tested frequency, 0.25 Hz, prominent secondary peaks at 0.50 Hz were observed, which were indicative of the presence of endogenous contractions (Fig. 2C, t ≈ 12 s, see Supplementary Text).

Enhanced swimming speeds up to 2.8 times using onboard microelectronics to drive frequencies Swimming trials with the implanted system were conducted in a 1.8 m × 0.9 m × 0.9 m saltwater tank. As illustrated in Fig. 3A, animals were introduced at the top of the tank and observed swimming downward to the bottom of the tank. We tracked bell displacements at external stimulation frequencies from 0 Hz (swim controller inactive, endogenous pulses only) to 1.00 Hz, which bracketed the observed endogenous swimming frequencies. The measured swimming speeds were normalized by the body diameter to account for variations in animal size (n = 6 animals). The normalized swimming speed scaled by the mean of the normalized 0-Hz speed (in the absence of stimulation) is subsequently referred to as the enhancement factor. Figure 3B plots enhancement factors and speeds, which both peaked at 0.50- or 0.62-Hz external stimulation for all jellyfish. The maximum peak enhancement factor was 2.8 ± 0.3 times the natural swimming speed of the animals. Fig. 3 Externally driven swimming can increase speeds up to 2.8 times. (A) Schematic of vertical free-swimming experiments. Jellyfish (n = 6, resting bell diameters d ranging from 13.0 to 19.0 cm) swam downward starting from rest in a 1.8 m × 0.9 m × 0.9 m artificial seawater tank. Videos were recorded using a single camera at 60 fps. (B) Swimming speeds and enhancement factors for swim controller frequencies at 0, 0.25, 0.38, 0.50, 0.62, 0.75, 0.88, and 1.00 Hz. Each animal is represented by a different color curve, and the size range per animal reflects changes in bell growth over time (experiments were conducted over several days). Normalized speeds (body diameters per second) are indicated on the right ordinate axis. The enhancement factor is defined as the normalized swimming speed scaled by the mean of the normalized 0-Hz speed (in the absence of stimulation, in which the swim controller is embedded but inactive). We observed a trend correlating greater performance enhancements with smaller, less oblate jellyfish, as determined by the bell diameter and fineness ratio (i.e., bell height to diameter ratio). The maximum observed enhancement occurred for the animal with the smallest bell diameter (13.0 cm) and greatest fineness ratio (0.3). Conversely, the smallest peak enhancement (1.3 ± 0.1) occurred for the animal with the largest bell diameter (18.2 to 19.0 cm) and smallest fineness ratio (0.2). For endogenous swimming in vertical free-swimming experiments, the natural observed swimming frequency was 0.24 ± 0.11 Hz (n = 8). Although this mean frequency is comparable to the controller-driven frequency of 0.25 Hz, the variability of the endogenous swimming frequency was higher (Fig. 2, E and F) compared to externally driven swimming frequencies (Fig. 2G), as previously noted. This irregular swimming results in slower overall speeds, in contrast to the higher swimming speeds observed under external stimulation at frequencies with comparable mean values to natural swimming. With increasing swimming frequencies imposed by the controller, swimming speeds increased until a biological constraint was reached, which occurred when the driven input f was greater than a critical frequency (f crit ) corresponding to the sum of the contraction (t c ) and relaxation times (t r ), i.e., f crit = 1/(t c + t r ). In these cases, the muscle could not fully relax to allow the subumbrellar cavity volume to refill completely before subsequent contractions (see movie S1). Hence, the amount of incremental thrust generated decreased, leading to decreasing swimming speeds at higher f values. These trends in enhancement versus body size, fineness ratio, and swimming frequency were predictable based on a new theoretical model that captures the tradeoff between faster swimming speeds and shorter muscle relaxation times at higher swimming frequencies (see the “Mechanistic model” section in Materials and Methods, Supplementary Text, and figs. S2 to S4). This model extends the work from previous hydrodynamic models to incorporate more biologically relevant swimming kinematics and morphological parameters, including inactive periods at lower swimming frequencies and truncated muscle contractions at higher frequencies (see the “Adaptations to the model” section in Materials and Methods for further improvements).

Device power consumption: 10 to 1000 times more energy efficient than existing aquatic robots The artificially controlled jellyfish requires both external power from the microelectronic system and internal power from the animals’ own metabolism. As a fair comparison to other robots, including bottom-up robotic constructs that incorporate cells and neglect adenosine 5′-triphosphate (ATP) consumption, we will first discuss the external power consumption of the microelectronic components, followed by a discussion of the animals’ energy expenditure at externally driven frequencies. The microelectronic system of the biohybrid robotic jellyfish consumed 0.06 ± 0.01, 0.13 ± 0.03, and 0.12 ± 0.09 W kg−1 when driven at 0.25, 0.50, and 0.88 Hz, respectively. Compared to existing robots, this biohybrid robot uses up to 1000 times less external power (from the 10-mAh battery in the swim controller) per mass of the biohybrid robot (comprising the animal and microelectronic system). Figure 4 illustrates a comparison of this system with swimming robots reported in literature (see table S3 for values and calculations). Marker shapes indicate the type of aquatic robot, from robots composed of biological tissue, such as the medusoid and robotic ray made from rat cardiomyocytes seeded on silicon scaffolds (9, 10), to purely mechanical robots, including bioinspired robots (3, 7, 33, 34) and AUVs (35). Marker colors indicate the type of propulsion, including medusan (jellyfish swimming), thunniform (fish swimming), rajiform (ray swimming), and propeller-driven locomotion. Fig. 4 External power requirements of the biohybrid robotic jellyfish compared to other swimming robots in literature. Marker shapes illustrate the type of aquatic robot, from biological soft robots such as the medusoid and robotic ray made from rat cardiomyocytes seeded on silicon scaffolds (9, 10) to purely mechanical robots, including bioinspired robots (3, 7, 33, 34) and an AUV (35). Marker colors illustrate the type of propulsion, including medusan (jellyfish swimming), thunniform (fish swimming), rajiform (ray swimming), and propeller-driven (AUVs). The external power (from the 10-mAh battery in the swim controller) per mass of the biohybrid robot (comprising the animal and the microelectronic system) is plotted versus swimming speed as red crosses. For actual values and details on the calculations, see table S3. The present biohybrid robotic jellyfish performs at similar swimming speeds (3.0 to 8 cm s−1) to those of other bioinspired mechanical robots, such as Robojelly at 3.1 cm s−1 and Jennifish at 3.0 cm s−1 (3, 33). However, there is a tradeoff between normalized power consumption and speed. For example, the mechanical soft robotic fish SoFi can swim 10-fold faster but with a 100-fold increase in normalized power consumption (34). A similar trend is observed with the REMUS 100 AUV, which uses 125 W kg−1 to travel 1.5 m s−1 (35). Both types of underwater robots—low-power robots such as this biohybrid robot and high-power robots with faster swimming speeds such as AUVs—can be appropriate for ocean monitoring purposes. However, the present work can potentially enable newer underwater vehicles to observe the environment over significantly longer durations and, similarly to SoFi, might be used with minimal disturbances to the environment because the body form and generated wakes are similar to those of natural organisms. Moreover, because jellyfish do not have a swim bladder, they can reach 3700-m depths in the ocean (15). Only the microelectronics will require hardening for operation at high pressures. Low cost and ease of use. In addition to low external power consumption per mass of the biohybrid robot, this microelectronic system uses less than $20 of off-the-shelf, readily available components. Furthermore, the extensive spatiotemporal characterizations conducted here enable new users to embed the device into live animals easily because electrode location is nonspecific, and animals recover immediately after experiments (see the “Ethical considerations” section in Supplementary Text).