Abstract

New types of robots inspired by biological principles of assembly, locomotion, and behavior have been recently described. In this work we explored the concept of robots that are based on more fundamental physical phenomena, such as fluid dynamics, and their potential capabilities. We report a robot made entirely of non-Newtonian fluid, driven by shear strains created by spatial patterns of audio waves. We demonstrate various robotic primitives such as locomotion and transport of metallic loads—up to 6-fold heavier than the robot itself—between points on a surface, splitting and merging, shapeshifting, percolation through gratings, and counting to 3. We also utilized interactions between multiple robots carrying chemical loads to drive a bulk chemical synthesis reaction. Free of constraints such as skin or obligatory structural integrity, fluid robots represent a radically different design that could adapt more easily to unfamiliar, hostile, or chaotic environments and carry out tasks that neither living organisms nor conventional machines are capable of.

1 Introduction

Recent developments in robotics and associated fields such as materials sciences and engineering have led to a paradigm shift towards unconventional robots inspired by living organisms and their properties and capabilities [2,,5, 7, 8]. Such robots are highly adaptable for tasks in challenging environments. For example, soft, solid-state robots fabricated from elastic polymers and devoid of hard parts or joints could potentially withstand high mechanical loads and survive crushing and bending forces lethal to conventional machines.

However, the living organisms that inspire these robots are still largely constrained by structural and functional biological barriers. For example, skins and exoskeletons, which protect the integrity and chemical composition of organisms, substantially limit their morphological flexibility. Moreover, even organisms devoid of skeleton (Mollusca) tend to adhere to well-defined outlines and shapes, and while resilient to bending and crushing, they are highly vulnerable to cutting and shearing forces. These faults could impair the survivability of robots designed to operate in highly dynamic or hostile environments in which the conditions cannot be predicted a priori. But they could be addressed by shifting to nonliving matter as the source of inspiration for robots with a radical, distinctly non-biological set of behaviors and capabilities. In this work we have focused on fluid dynamics as a paradigm for this challenge.

Some properties required of a robot, such as motion and the ability to perform mechanical work, are natural to fluids; others, such as automatic actuation in response to cues, are less trivial. Most importantly, reliable and reproducible control of unbound fluids at high Reynolds numbers is extremely challenging. The mechanical properties and behavior of Newtonian (or approximately Newtonian) fluids, such as water under standard conditions, are largely invariant under applied forces and most other environmental cues. However, in contrast, non-Newtonian fluids exhibit diverse, peculiar behaviors deriving from the nonlinear relation between fluid deformation rate and shear stress, which is particular to each system [9]. The physics underlying properties of non-Newtonian fluids, such as shear thinning or thickening, is still only partly understood [1]. Despite that, as in other fields, incompleteness of theoretic understanding does not prevent successful implementation; hence, it is possible to assemble specific components to yield a non-Newtonian fluid with the desired properties in a manner analogous to programming a robot for a specific purpose. In the present study we designed a non-Newtonian fluid robot and studied its responses in various programming contexts.

2 Results

To prove the feasibility of a non-Newtonian fluid robot that can be reliably and reproducibly fabricated and controlled, we examined various fluids with diverse compositions. For our prototype we chose a colloidal dispersion of starch microparticles in an aqueous fructose solution (concentration 58% w/v; mean particle diameter 6.41 μm; see  Appendix 1), to which shear strains were anisotropically applied through the surface by a 4 × 4 array of individually controlled audio transducers (Figure 1). To construct the control apparatus, the following parts were used: speakers (10-cm diameter, 40 W, 8 Ω), a Hewlett-Packard 33120A signal generator, 90-W AST 4x25 amplifiers, mini DVR cameras (25 fps), a digital video recording system, an oscilloscope (Agilent Technologies DSO1052B, 50 MHz, two-channel), and a Sensemode™ voltage sensor and data transceiver. The surface was made from standard artist canvas sheets (50 cm × 50 cm with wooden frame).

Figure 1. 

Experimental control setup. The signal generator (Hewlett-Packard, 100-MHz max frequency) employed as source was connected to four amplifier units, each separately controlling four individual transducers/speakers (specifications to be completed) numbered 1–16. An Agilent oscilloscope was used to visualize sine wave properties. Bottom left photo shows the signal generator (unit on the right), to which four amplifier units are connected (bottom racks), and an oscilloscope (unit on top). Bottom right photo shows the 4 × 4 transducer array controlled by the amplifiers. Three video cameras were mounted around the system, providing X, Y, and Z axis coverage. Data was stored in real time on a DVR unit.

Figure 1. 

Experimental control setup. The signal generator (Hewlett-Packard, 100-MHz max frequency) employed as source was connected to four amplifier units, each separately controlling four individual transducers/speakers (specifications to be completed) numbered 1–16. An Agilent oscilloscope was used to visualize sine wave properties. Bottom left photo shows the signal generator (unit on the right), to which four amplifier units are connected (bottom racks), and an oscilloscope (unit on top). Bottom right photo shows the 4 × 4 transducer array controlled by the amplifiers. Three video cameras were mounted around the system, providing X, Y, and Z axis coverage. Data was stored in real time on a DVR unit.

In order to reliably and reproducibly control the fluid robot, we preferred an empirical approach over simulations, and iteratively sought transducer usage sequences that manipulated the fluid as desired. We quickly observed that audio waves at specific outputs and frequencies created clearly visible viscosity gradients within the fluid, which drove the robot in the downhill direction between arbitrary points on the surface (Figure 2a,  Appendix 2, and Videos S1–S3, which can be found at www.mitpressjournals.org/doi/suppl/10.1162/ARTL_a_00194). At higher velocities produced by higher strains, the robot was significantly more condensed and rigid (Figure 2bd; images were computationally analyzed as described in  Appendix 3). Repetitive experiments demonstrated that although the fluid could not be controlled to the extent of identical replication of previous shapes and trajectories, its statistical behavior was highly reproducible.

Figure 2. 

Fluid robot moving and carrying load. (a) paths of a fluid robot moving from point A to point B in 10 independent experiments. X and Y positions are in millimeters. (b) correlation between robot surface area and velocity, two parameters dependent on robot strain. Each point represents a second in a typical experiment. (c) robot surface area (in square centimeters) changes along a path from A to B in a typical experiment. (d) snapshots from a representative experiment on a fluid robot moving from A to B to A (left panel, schematic; scale bar, 10 cm). (e) snapshots from a representative experiment on a fluid robot carrying a load (in this case a 100-g metal weight; scale bar, 10 cm).

Figure 2. 

Fluid robot moving and carrying load. (a) paths of a fluid robot moving from point A to point B in 10 independent experiments. X and Y positions are in millimeters. (b) correlation between robot surface area and velocity, two parameters dependent on robot strain. Each point represents a second in a typical experiment. (c) robot surface area (in square centimeters) changes along a path from A to B in a typical experiment. (d) snapshots from a representative experiment on a fluid robot moving from A to B to A (left panel, schematic; scale bar, 10 cm). (e) snapshots from a representative experiment on a fluid robot carrying a load (in this case a 100-g metal weight; scale bar, 10 cm).

The described layout divides a 0.36-m2 area into 16 auxels (audio pixels) of 225 cm2 each. Even at this low spatial resolution, the fluid robot could be accurately controlled with minimal transducer utilization. For example, we found that the minimal number of transducers required for driving the robot along a straight path is two, and that this pair of transducers was sufficient to drive the robot across the entire surface length ( Appendix 5). Smaller auxels and alternative layouts (e.g., honeycomb arrayed auxels) could likely allow finer tuning of robot behavior.

Most robots are designed with the ability to do some form of mechanical work. We therefore investigated the ability of the fluid robot to carry loads that are significantly heavier than itself—in this case, metal weights. Our initial studies showed clear reduction in the velocity of the robot upon contact with the target weight (e.g., from 1 cm/s to 0.32 cm/s), indicating that a viscous drag force is created between the robot and the weight. Our calculations yielded a drag force of 0.07 N, which (assuming an object-to-surface kinetic friction of ≈0.4, and for a robot of the size used here and of the same colloidal dispersion) is sufficient to carry objects of ≈176 g—almost 6-fold heavier than the robot itself. This was confirmed by our measurements, in which the reduction in velocity of the loaded robot is visible (Figure 2e, Video S4 in the Online Supplement). Although the drag coefficient is not constant, our measurements were in good agreement with an approximation of the drag equation based on constant CD (Table 2 in  Appendix 2). Analysis of robot locomotion using side-mounted cameras revealed a distinct behavior observed in all experiments, involving the robot's leading end (head) assuming a hump shape, sometimes up to 10-fold higher than the robot's tail (Figure 3a). Our video analysis showed that the tail (the rear part relative to the motion vector) remains consistently fluid throughout the experiment and continuously moves towards the head, which compresses and thickens as a result. This enables the tail fluid to physically climb on top of the head, creating the hump, which was consequently prominent when acceleration was high ( Appendix 2).

Figure 3. 

Fluid robot shapeshifting, splitting, and percolating. (a) Humps generated in the leading end of robots, shown from 10 overlaid experiments at high-acceleration time points. Inset shows a representative snapshot. (b) Frame-by-frame change in the fluid robot's shape during a typical experiment, measured as the maximal aspect ratio (longest to shortest robot dimensions). (c) Splitting of the fluid robot into two separate fragments, each controlled independently, finally merging back to a single robot (scale bar, 10 cm). (d) A representative experiment showing the robot percolating through a handmade metal grating with a 1-cm gap length (scale bar, 10 cm).

Figure 3. 

Fluid robot shapeshifting, splitting, and percolating. (a) Humps generated in the leading end of robots, shown from 10 overlaid experiments at high-acceleration time points. Inset shows a representative snapshot. (b) Frame-by-frame change in the fluid robot's shape during a typical experiment, measured as the maximal aspect ratio (longest to shortest robot dimensions). (c) Splitting of the fluid robot into two separate fragments, each controlled independently, finally merging back to a single robot (scale bar, 10 cm). (d) A representative experiment showing the robot percolating through a handmade metal grating with a 1-cm gap length (scale bar, 10 cm).

A fluid can change shape in ways that are impossible for living organisms, but may be highly desired in various settings. The morphological diversity of the fluid robot is limited mainly by surface tension, which depends on volume and composition and could be readily programmed by proper fluid assembly. The surface tension and thus the volume must be kept within a limited range, robots beneath it being too rigid and brittle to control, and ones above it too large to maintain integrity; but within this range the fluid exhibited remarkable shape flexibility, including changing aspect ratios and expanding its surface area by a factor of approximately 36 (Figure 3b, Video S5 in the Online Supplement). In addition, the robot can be split by vibration directed perpendicularly at the center of mass into two or more separate smaller robots, so long as none of them falls beneath the lower volume threshold. The parts can be controlled individually and be driven to merge to form a single robot again (Figure 3c, Video S6 in the Online Supplement). Similarly, the robot can percolate through porous barriers and gratings (Figure 3d, Videos S7, S8 in the Online Supplement).

We were interested in the possibility of programming the robot to exhibit the equivalents of computational and logic primitives. One straightforward example is to set the robot to function as a finite counter, halting or changing behavior when the defined sum has been reached. In this example the input can be matter directly absorbed by the robot, changing its chemical composition and subsequently its fluid behavior. To demonstrate this, we used aluminum oxide dough packets as input bits, and programmed the robot to halt upon absorbing three discrete packets out of four. This was done by assembling a robot that would become too rigid following the assimilation of exactly three packets (Figure 4a, Video S9 in the Online Supplement). Similar behaviors could be achieved with robot–robot collisions rather than robot–bit collisions.

Figure 4. 

Fluid robot counting and driving chemical synthesis. (a) Snapshots of a fluid robot counting three input bits and halting. Each input bit (white arrowheads) is an aluminum oxide dough packet. Graph shows the change in robot surface area (in square centimeters) and the average velocity between bit assimilation events (corresponding to snapshot timecodes). Counter status is presented in the bottom right corners. Scale bar, 10 cm. (b) A schematic of the chemical reaction carried out in this experiment (EDC, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide; green star, fluorescent dye). Each component is dissolved in a separate robot (R1–R3), and the product is examined by adsorption onto streptavidin microparticles and solid-phase flow cytometry. Histogram shows samples from R3 before synthesis and from R3 after (mixed with R1 + R2), following precipitation of starch microparticles.

Figure 4. 

Fluid robot counting and driving chemical synthesis. (a) Snapshots of a fluid robot counting three input bits and halting. Each input bit (white arrowheads) is an aluminum oxide dough packet. Graph shows the change in robot surface area (in square centimeters) and the average velocity between bit assimilation events (corresponding to snapshot timecodes). Counter status is presented in the bottom right corners. Scale bar, 10 cm. (b) A schematic of the chemical reaction carried out in this experiment (EDC, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide; green star, fluorescent dye). Each component is dissolved in a separate robot (R1–R3), and the product is examined by adsorption onto streptavidin microparticles and solid-phase flow cytometry. Histogram shows samples from R3 before synthesis and from R3 after (mixed with R1 + R2), following precipitation of starch microparticles.

This possibility leads to an attractive feature of fluid robotics: the integration of logic and chemistry, a concept raised previously in microfluidic systems with bubbles functioning as bits that carry chemical payloads [6]. As a proof-of-concept experiment we chose the carbodiimide-mediated cross-linking of amine-modified DNA with a protein. In this reaction, the carbodiimide reacts with carboxylic acid groups in the protein (mainly on the acidic amino acids aspartic acid [Asp/D] and glutamic acid [Glu/E]), forming an active O-acylisourea intermediate that is then displaced by nucleophilic attack from the primary amines on the modified DNA strands. The primary amine forms an amide bond with the original carboxyl group, and a carbodiimide by-product is released as a soluble urea derivative. Three discrete robots (volume of 10 mL each) carrying the reaction components—protein, amine-modified DNA, and the crosslinker (1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), at a 1 : 10 : 5000 molar ratio, respectively—were driven to collide in a stepwise manner, with robot movement providing constant mixing. The entire experiment was performed at room temperature. Successful synthesis of the product was confirmed by solid-phase flow cytometry and spectrophotometry (Figure 4b).

3 Discussion

Contemporary works in the field of unconventional robotics draw from a wide range of biological shapes and mechanisms of actuation. While all these robots exhibit peculiar capabilities, more diverse than those of conventional ones, they still adhere to the basic physics and architecture of living bodies, which constrains their behavior. For example, all living systems, from the single cell to complex organisms, have barriers protecting their internal environment from the outer one, such as membranes and skins, which crucially limit the shape diversity that a living organism can assume. Moreover, any organism has to maintain its structural integrity to prevent injury or death. Interestingly, bio-inspired robots inherit these constraints through their design, and they too must remain intact in order to remain functional and usually do not change their shape profoundly. This raises the question whether radically different designs of robots exist that are free of these limits, and could accordingly carry out tasks that are challenging for more conventional robots.

In this work we report a proof of concept for a reliably and reproducibly controlled robot based on the properties and behaviors of non-Newtonian fluids. Using non-Newtonian fluids as the basis of this design was crucial to enable arbitrary control over shape and motion, which was easily obtained by using audio waves created by the user. We describe here a “program” as a graphic representation of a temporal sequence of transducer outputs leading to a specific behavior ( Appendices 4– 5).

Although the physics of non-Newtonian fluids is still not well understood, it is possible to utilize the known observed behaviors and responses of these fluids in the design of robotic devices. We used an empirical approach to assemble a non-Newtonian fluid with the desired set of properties as well as to define control profiles for various tasks. No simulations were used in this process, but rather iterative trial and error, in which fluid composition was measured by observed performance in a process of directed evolution. Similarly, the control profiles were written after systematic evaluation of how the fluid responds to different spatiotemporal audio patterns. We believe that improved understanding of non-Newtonian fluids in the near future will simplify this process and enable reliable prediction of the behavior of specific compositions and their responses to specific audio wave patterns, and bring this process closer to conventional programming.

Fluid robots could have many interesting applications, and our study raises some interesting directions. For example, it highlights the fascinating possibility of utilizing fluid robots as components in a large, complex chemical assembly line. In this regard, further work could produce robots with specialized functions such as waste removal, affinity chromatography, dialysis from robot to robot, and more. The surface itself could include regions with heating or cooling, fluid guides and splitters, and similar topography-based rather than strain-based controls. Fluid robots could find applications in search and rescue tasks. For example, fluid robots could percolate and drip into areas where access is highly limited, such as the debris of collapsed buildings or mines, merging again below to form a robot that could carry weights and perform work.

Using audio waves to guide the robot on a surface might seem an essential flaw of the system, limiting its potential use to surfaces through which audio waves can be transmitted. However, further studies could likely extend the current design in many ways. Fluid robots can be controlled by unidirectional audio beams transmitted from commercially available devices. They can be driven to crawl upwards on inclined surfaces and climb to higher areas onboard continuous surfaces. The current proof-of-concept study did not attempt to assemble fluids with additional properties, such as magnetic or charged fluids which could be controlled by magnetic and electric fields orthogonal to mechanical shear forces. Overlaying similar modes of control would likely lead to very elaborate and capable designs.

Finally, further studies into mechanical devices made of non-Newtonian fluids could elucidate much of the physics underlying non-Newtonian fluid dynamics, and lead to improved designs with higher capabilities than the prototype presented here. It would be interesting to design fluid robots coated with a cell-inspired lipid bilayer, which could provide desired properties such as reducing water loss by evaporation, hydrophobic or electrostatic repulsion between robots, or even primitive mechanisms for self–nonself recognition, when two or more populations of robots navigate and operate in parallel within the same system, avoiding incidental mixing. In this regard, fluid robots could show unexpected similarities to primitive life forms.

Acknowledgments

The authors wish to thank Larisa Zlatin, Sivan Friedman, and all members of the Bachelet lab for valuable discussions and technical assistance.

References

1
Cheng
,
X.
,
McCoy
,
J. H.
,
Israelachvili
,
J. N.
, &
Cohen
,
I.
(
2011
).
Imaging the microscopic structure of shear thinning and thickening colloidal suspensions
.
Science
,
333
,
1276
1279
.
2
Felton
,
S.
,
Tolley
,
M.
,
Demaine
,
E.
,
Rus
,
D.
, &
Wood
,
R.
(
2014
).
A method for building self-folding machines
.
Science
,
345
,
644
646
.
3
Laschi
,
C.
,
Mazzolai
,
B.
,
Mattoli
,
V.
,
Cianchetti
,
M.
, &
Dario
,
P.
(
2009
).
Design of a biomimetic robotic octopus arm
.
Bioinspiration & Biomimetics
,
4
,
015006
.
4
Ma
,
K. Y.
,
Chirarattananon
,
P.
,
Fuller
,
S. B.
, &
Wood
,
R. J.
(
2013
).
Controlled flight of a biologically-inspired insect scale robot
.
Science
,
340
,
603
607
.
5
Morin
,
S. A.
,
Shepherd
,
R. F.
,
Kwok
,
S. W.
,
Stokes
,
A. A.
,
Nemiroski
,
A.
, &
Whitesides
,
G. M.
(
2012
).
Camouflage and display for soft machines
.
Science
,
337
,
828
832
.
6
Prakash
,
M.
, &
Gershenfeld
,
N.
(
2007
).
Microfluidic bubble logic
.
Science
,
315
,
832
835
.
7
Rubenstein
,
M.
,
Cornejo
,
A.
, &
Nagpal
,
R.
(
2014
).
Programmable self-assembly in a thousand robot swarm
.
Science
,
345
,
795
799
.
8
Shepherd
,
R. F.
,
Ilievski
,
F.
,
Choi
,
W.
,
Morin
,
S. A.
,
Stokes
,
A. A.
,
Mazzeo
,
A. D.
,
Chen
,
X.
,
Wang
,
M.
, &
Whitesides
,
G. M.
(
2011
).
Soft multigait robot
.
Proceedings of the National Academy of Sciences of the U.S.A.
,
108
,
20400
20403
.
9
Tropea
,
C.
,
Yarin
,
A. L.
, &
Foss
,
J. F.
(Eds.) (
2007
).
Springer handbook of experimental fluid mechanics
.
Berlin
:
Springer
.

Appendix 1: Material Screening

The final assembly protocol for robots was as follows: 58% w/v starch microparticles (Figures 56), 7.15% v/v fructose-glucose solution (40% w/v fructose, 27% w/v glucose in DW), 7.15% v/v color solution (as described below). Color was introduced to enhance robot contrast for video analysis. See Table 1 and Figure 7.

Figure 5. 

Dynamic light scattering analysis of starch particle size.

Figure 5. 

Dynamic light scattering analysis of starch particle size.

Figure 6. 

Zeta potential analysis of starch microparticles. The zeta potential is the electrical potential difference between the stationary fluid layer on the surface of each particle, and the fluid not adjacent to the particle (the dispersion fluid). Its value is a measure of the colloid's resistance to aggregation, with values between −5 and +5 indicating high tendency to coagulate and harden. The zeta potential is a significant property of a colloidal fluid, and could be key in future designs once a quantitative link with the robot's performance is defined. Y-axis represents zeta potential values.

Figure 6. 

Zeta potential analysis of starch microparticles. The zeta potential is the electrical potential difference between the stationary fluid layer on the surface of each particle, and the fluid not adjacent to the particle (the dispersion fluid). Its value is a measure of the colloid's resistance to aggregation, with values between −5 and +5 indicating high tendency to coagulate and harden. The zeta potential is a significant property of a colloidal fluid, and could be key in future designs once a quantitative link with the robot's performance is defined. Y-axis represents zeta potential values.

Table 1. 

Fluid robot composition screening table. This table lists the main various compositions used in the empirical search for a controllable and reproducible non-Newtonian fluid. Material sources are entered in parentheses. Stirring included mainly vortexing for several minutes, aided by crushing with a metal spoon when needed.

Particle typeMain liquidAdditional liquids% solid particlesStirring methodComments and observations
Starch microparticles (6.4 μm) Distilled water 7.15% Food coloring (E-124 / E-143) 58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
7.15% fructose–glucose mixture (30% glucose, 35% fructose, 35% distilled water) 58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
33% gouache color (blue) 58% (40 g per 30 g liquid) Vortex mixer and metal spoon This combination fails to make NNF. 
7.17% v/v food coloring (E-124 / E-143) and 7.15% v/v fructose–glucose mixture 58% (40 g per 30 g liquid) Vortex mixer and metal spoon This combination was used in the majority of the experiments, as it is both stable and has good visibility on cameras. 
Tap water 7.17% v/v food coloring (E-124 / E-143) and 7.15% v/v fructose–glucose mixture 58% (40 g per 30 g liquid) Vortex mixer and metal spoon Liquid behavior differs from that of DW. 
Oil from canola seeds 7.17% v/v food coloring (E-124 / E-143) and 7.15% v/v fructose–glucose mixture 58% (40 g per 30 g liquid) Vortex mixer and metal spoon Hydrophobic behavior anticipated; however, NNF cannot be created using these materials. 
Tomato extract (35% tomato extract, 63% water, 2% glucose)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon This extract has pseudoplastic behavior; thus anti-dilatant behavior was expected. However, NNF cannot be created using these materials. 
TBE × 1 buffer (Promega)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
BupH × 2 buffer (Pierce)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon Liquid was made for synthesis experiment. 
Silica nanoparticles (200 nm) PEG (Sigma)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
Sea sand Distilled water  58% (40 g per 30 g liquid) Vortex mixer and metal spoon Weak NNF; cannot be controlled. 
Alumina nanoparticles (150 nm) Distilled water  58% (40 g per 30 g liquid) Vortex mixer and metal spoon NNF cannot be made with these materials. Gluey substance was formed. Was used for bits in counting experiment (with different concentrations). 
Particle typeMain liquidAdditional liquids% solid particlesStirring methodComments and observations
Starch microparticles (6.4 μm) Distilled water 7.15% Food coloring (E-124 / E-143) 58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
7.15% fructose–glucose mixture (30% glucose, 35% fructose, 35% distilled water) 58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
33% gouache color (blue) 58% (40 g per 30 g liquid) Vortex mixer and metal spoon This combination fails to make NNF. 
7.17% v/v food coloring (E-124 / E-143) and 7.15% v/v fructose–glucose mixture 58% (40 g per 30 g liquid) Vortex mixer and metal spoon This combination was used in the majority of the experiments, as it is both stable and has good visibility on cameras. 
Tap water 7.17% v/v food coloring (E-124 / E-143) and 7.15% v/v fructose–glucose mixture 58% (40 g per 30 g liquid) Vortex mixer and metal spoon Liquid behavior differs from that of DW. 
Oil from canola seeds 7.17% v/v food coloring (E-124 / E-143) and 7.15% v/v fructose–glucose mixture 58% (40 g per 30 g liquid) Vortex mixer and metal spoon Hydrophobic behavior anticipated; however, NNF cannot be created using these materials. 
Tomato extract (35% tomato extract, 63% water, 2% glucose)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon This extract has pseudoplastic behavior; thus anti-dilatant behavior was expected. However, NNF cannot be created using these materials. 
TBE × 1 buffer (Promega)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
BupH × 2 buffer (Pierce)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon Liquid was made for synthesis experiment. 
Silica nanoparticles (200 nm) PEG (Sigma)  58% (40 g per 30 g liquid) Vortex mixer and metal spoon  
Sea sand Distilled water  58% (40 g per 30 g liquid) Vortex mixer and metal spoon Weak NNF; cannot be controlled. 
Alumina nanoparticles (150 nm) Distilled water  58% (40 g per 30 g liquid) Vortex mixer and metal spoon NNF cannot be made with these materials. Gluey substance was formed. Was used for bits in counting experiment (with different concentrations). 
Figure 7. 

Behavior analysis of starch microparticle solutions. A range of aqueous solutions was prepared from starch microparticles in DW, and the solutions were sheared by vortexing the vessel in a standard vortex at 320 Hz. A score was given to the fluid's behavior based on the measured height of features produced in the fluid: 1, no shear thickening observed; 2–4, weak (0.5 cm), mild (0.5–1.5 cm), or strong (>1.5 cm) shear thickening; 5, no fluid behavior observed. This experiment was repeated with various solution volumes (blue, 200 mL; yellow, 30 mL; green, 10 mL).

Figure 7. 

Behavior analysis of starch microparticle solutions. A range of aqueous solutions was prepared from starch microparticles in DW, and the solutions were sheared by vortexing the vessel in a standard vortex at 320 Hz. A score was given to the fluid's behavior based on the measured height of features produced in the fluid: 1, no shear thickening observed; 2–4, weak (0.5 cm), mild (0.5–1.5 cm), or strong (>1.5 cm) shear thickening; 5, no fluid behavior observed. This experiment was repeated with various solution volumes (blue, 200 mL; yellow, 30 mL; green, 10 mL).

Appendix 2: Additional Movement Data

See Table 2 and Figures 8 and 9.

Table 2. 

Load carry measurements.

Weight (g)Time (s)
15 
10 23 
25 27 
50 51 
100 63 
150 72 
200 ∞ 
400 ∞ 
Weight (g)Time (s)
15 
10 23 
25 27 
50 51 
100 63 
150 72 
200 ∞ 
400 ∞ 
Figure 8. 

Robot moving between three points not on a direct path.

Figure 8. 

Robot moving between three points not on a direct path.

Figure 9. 

Robot hump outlines at high versus low acceleration. Summary of 10 independent movement (A to B) experiments. Each column is an experiment, while the rows are ordered as follows: top, velocity (red) and acceleration (blue) per frame; middle, robot outline (pixels) at maximum-acceleration frame ±5 frames (overlaid together); bottom, robot outline (pixels) at average-acceleration frame ±5 frames (overlaid together). Robot hump behavior is clearly shown in middle row samples, while the behavior is more random in the bottom row samples.

Figure 9. 

Robot hump outlines at high versus low acceleration. Summary of 10 independent movement (A to B) experiments. Each column is an experiment, while the rows are ordered as follows: top, velocity (red) and acceleration (blue) per frame; middle, robot outline (pixels) at maximum-acceleration frame ±5 frames (overlaid together); bottom, robot outline (pixels) at average-acceleration frame ±5 frames (overlaid together). Robot hump behavior is clearly shown in middle row samples, while the behavior is more random in the bottom row samples.

Appendix 3: Computational Image Analysis

A3.1 Data Acquisition and Basic Analysis

Video footage was analyzed by a custom-made MATLAB (Mathworks, R2013a) code, which uses the image-processing toolbox for extracting velocity, area, and path.

A3.2 Visual Representation of Speaker Activity

Recorded information was integrated graphically into the videos by our code. Each speaker is represented by a circle in proximity to its original location as it appears in the video. An active speaker appears in a red color in a shade related to the speaker intensity. The red shades are divided into 10 levels. Assumed maximal speaker intensity of 900 V; thus every red level represents 90 V. The higher the intensity, the stronger the red color.

A3.3 Calculating Aspect Ratio

We used the following algorithm to calculate the aspect ratio of the spot.

A3.4 Calculating Center, Area, and Velocity

As a preprocessing step before analysis, we converted each frame to a binary image to distinguish between the spot and the background. After detecting the spot, we were able to use MATLAB image-processing toolbox built-in functions to find its center and its area in each frame. Those functions get a binary image of an object and return the object's center in a 2D coordinate, and the area in pixels.

For calculating velocity we sampled two centers from two consecutive seconds and calculated the Euclidean distance between them. For calculating area per second and center per second we averaged the areas and the centers, respectively, obtained for an entire second. This appears in the following pseudocode:

Appendix 4: Control Profiles

See Figures 10,1112.

Figure 10. 

Mapping transducer outputs required for driving movement in straight path. Top panels show a robot (dark green) driven along a straight path from right to left, while active transducers are shown in red (color brightness corresponds to output level). Graphs show the output of each transducer lane (colored lane in inset) along the course of the experiment, with inset cell color corresponding to transducer position and graph color.

Figure 10. 

Mapping transducer outputs required for driving movement in straight path. Top panels show a robot (dark green) driven along a straight path from right to left, while active transducers are shown in red (color brightness corresponds to output level). Graphs show the output of each transducer lane (colored lane in inset) along the course of the experiment, with inset cell color corresponding to transducer position and graph color.

Figure 11. 

Mapping transducer outputs required for driving movement in straight path. This series of graphs is a zoom-in on the series in Figure 10, showing enhanced time axis.

Figure 11. 

Mapping transducer outputs required for driving movement in straight path. This series of graphs is a zoom-in on the series in Figure 10, showing enhanced time axis.

Figure 12. 

Mapping transducer outputs required for carrying load.

Figure 12. 

Mapping transducer outputs required for carrying load.

Appendix 5: Robot Assembly and Control Protocols

Scheme 1: A→B movement experiments: See Figure 13. A robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface at point A, yielding a robot ≈10 cm in diameter. Transducers 2 and 3 were activated in pulses for 30 s on average (time changes slightly between experiments) until the robot reached the third speaker lane. Transducers 10 and 11 were then activated in pulses for a time length of 10 s on average up until the robot reached point B. Transducers 2 and 3 were to remain active all the time.

Scheme 2: A→B→A experiments: See Figure 14. As in the A→B experiments, a robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface at point A, yielding a robot ≈10 cm in diameter. Transducers 2 and 3 were activated in pulses for 30 s on average (time changes slightly between experiments) until the robot reached the third speaker lane. Transducers 10 and 11 were then activated in pulses for a time length of 10 s on average up until the robot reached point B. At this point, all transducers were shut off, and transducers 14 and 15 became active for ≈30 s on average. After the robot reached speaker lane 2, transducers 6 and 7 were activated in the same manner as before for ≈10 s on average, pushing the robot back to point A.

Scheme 3: A→B→C experiments: See Figure 15. A robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface at point A, yielding a robot ≈10 cm in diameter. Transducers 2 and 3 were activated in pulses for 30 s on average (time changes slightly between experiments) until the robot reached the third speaker lane. Transducers 10 and 11 were then activated in pulses for a time length of 10 s on average up until the robot reached point B. At this point, all transducers were shut off, and transducers 11 and 15 became active for ≈20 s on average up until the robot reached point C.

Scheme 4: Load-carrying experiments: See Figure 16. A robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface at point A, yielding a robot ≈10 cm in diameter. A 100-g weight was placed upon the surface 10 cm in front of the robot. Transducers 2 and 3 were activated in pulses for ≈20 s on average (time changes slightly between experiments) until the robot reached and engulfed the weight. The transducers were kept on for ≈20 s more on average, until reaching line 3. Transducers 10 and 11 were then activated in pulses for a time length of 10 s on average up until the robot reached point B.

Scheme 5: Splitting and re-merging experiments: See Figure 17. A robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface between transducers 1, 2, and 5, yielding a robot ≈10 cm in diameter. Transducers 1, 2, and 5 were activated all at once for 10 s until the robot split into two parts. Transducer 5 was activated in pulses for a time length of 20 s, pushing the closer piece of the robot into the piece farther away. The two pieces merged back into a single robot.

Scheme 6: Shapeshifting experiments: See Figure 18. A robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface between transducers 2 and 7, yielding a robot ≈10 cm in diameter. Transducers 7 and 2 were activated in pulses for 20 s until the robot changed its shape into an elongated form. The transducers were then shut down, allowing the robot to return to a round shape.

Scheme 7: Counting experiment: See Figure 19. As in the A→B experiments, a robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface at point A, yielding a robot ≈10 cm in diameter. Four alumina bits (10 g of alumina nanoparticles in 10 mL of distilled water each) were placed along the path from point A to point B. Transducers 2 and 3 were activated in pulses for 100–110 s, pushing the robot towards point B while collecting the alumina bits on its way. At the third bit, the robot could not continue its movement.

Scheme 8: Percolation (movement through grating) experiment: See Figure 20. A robot (30 mL of 58% w/v starch microparticles, 7.15% v/v E-124 / E-143 coloring, 7.15% v/v fructose–glucose mixture) was applied onto the speaker surface at point A, yielding a robot of ≈10 cm in diameter. Gratings (five bolts 2 cm apart, connected by a wooden board) were placed upon the surface, about 10 cm away from the robot. Transducers 2 and 3 were activated in pulses for ≈60 s until the robot passed through the gratings.

Scheme 9: Chemical synthesis (biosynthesis): See Figure 21. As this experiment involved DNA and proteins, the robot composition was altered slightly. Three robots were made, each mixed with substances as shown in Table 3. 15 mL of R1 and 15 mL of R2 were applied onto the speaker surface 15 cm away from each other. Transducers 2 and 3 were activated in pulses for 20 s, followed by transducer 6. R1 and R2 were then merged into R1+2. Transducers 6 and 7 were activated, thus making R1+2 mix itself. At this point, 15 mL of R3 was applied onto the surface between transducers 6 and 10. Transducers 8 and 12 were then activated in pulses for about 15 s, pushing R1+2 onto R3, making the two robots merge. Transducers 7 and 11 were activated, allowing the robot to mix for 20 s. A sample of 20 mL was taken from the mixture and run in a centrifuge at 17,000g for 5 min. The supernatant was collected and mixed with streptavidin beads for 15 min. This final mixture was then tested with an Accuri BD6 FACS. BupH buffer and EDC were purchased from Pierce.

Table 3. 
Robot R1Robot R2Robot R3
Robot composition 60% w/v starch microparticles, 20% v/v BupH × 1 buffer, 13% v/v distilled water, 7% v/v E-143 60% w/v starch microparticles, 20% v/v BupH × 1 buffer, 13% v/v distilled water, 7% v/v E-143 60% w/v starch microparticles, 20% v/v BupH × 1 buffer, 13% v/v distilled water, 7% v/v E-143 
Loaded substance 20 mg albumin after DyLight treatment (performed according to the manufacturer's instructions) 200 mg EDC 1 mL 100-μM poly-T ssDNA modified with biotin (3′) and amine (5′) 
Robot R1Robot R2Robot R3
Robot composition 60% w/v starch microparticles, 20% v/v BupH × 1 buffer, 13% v/v distilled water, 7% v/v E-143 60% w/v starch microparticles, 20% v/v BupH × 1 buffer, 13% v/v distilled water, 7% v/v E-143 60% w/v starch microparticles, 20% v/v BupH × 1 buffer, 13% v/v distilled water, 7% v/v E-143 
Loaded substance 20 mg albumin after DyLight treatment (performed according to the manufacturer's instructions) 200 mg EDC 1 mL 100-μM poly-T ssDNA modified with biotin (3′) and amine (5′) 

Author notes

Contact author.

∗∗

Faculty of Life Sciences and Institute of Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat Gan 52900, Israel.

These authors contributed equally to this work.

Current affiliation: Augmanity Labs, Rehovot Science Park, Rehovot, Israel.

§

8 HaMada Street, Rehovot Science Park, Rehovot 7670308, Israel. E-mail: dogbach@gmail.com