Marc Z. Miskin

To explore information's role in the physics of living systems, experimentalists have recently turned to making materials from robots. These systems offer unique opportunities because they are easy to control and interpret, yet each machine retains the capacity to sense and compute. Here, we review recent work on robotic matter, emphasizing how internal states, local sensing, and feedback at the microscale enable macroscale properties that are fundamentally distinct from traditional condensed matter or other active matter systems. We argue that this field is poised to evolve rapidly, thanks to technological innovations including semiconductor miniaturization, heterogeneous materials integration, and low-power computation. Finally, we highlight outstanding experimental and theoretical challenges that robots are well positioned to address, including the tradeoffs between robot size and intelligence and the difficulty of preserving information flows when robot actions are coarse-grained into macroscopic variables.

Although miniaturization has been a goal in robotics for nearly 40 years, roboticists have struggled to access submillimeter dimensions without making sacrifices to onboard information processing because of the unique physics of the microscale. Consequently, microrobots often lack the key features that distinguish their macroscopic cousins from other machines, namely, on-robot systems for decision-making, sensing, feedback, and programmable computation. Here, we take up the challenge of building a robot comparable in size to a single-celled paramecium that can sense, think, and act using onboard systems for computation, sensing, memory, locomotion, and communication. Built massively in parallel with fully lithographic processing, these microrobots can execute digitally defined algorithms and autonomously change behavior in response to their surroundings. Combined, these results pave the way for general-purpose microrobots that can be programmed many times in a simple setup and can work together to carry out tasks without supervision in uncertain environments.

Abstract Field-based reactive control provides a minimalist, decentralized route to guiding robots that lack onboard computation. Such schemes are well suited to resource-limited machines like microrobots, yet implementation artifacts, limited behaviors, and the frequent lack of formal guarantees blunt adoption. Here, we address these challenges with a new geometric approach called artificial spacetimes. We show that reactive robots navigating control fields obey the same dynamics as light rays in general relativity. This surprising connection allows us to adopt techniques from relativity and optics for constructing and analyzing control fields. When implemented, artificial spacetimes guide robots around structured environments, simultaneously avoiding boundaries and executing tasks like rallying or navigation, even when the field itself is static. We augment these capabilities with formal tools for analyzing what robots will do and provide experimental validation with silicon-based microrobots. Combined, this work provides a new framework for generating composed robot behaviors with minimal overhead.

Chronic medical implants require biocompatible encapsulation to protect electronics from biofluids, and vice versa. Recent efforts have focused on thin-film encapsulation for its lightweight and flexible properties. Atomic layer deposition (ALD) and plasma-enhanced chemical vapor deposition (PECVD) silicon dioxide are two potential materials, as they are CMOS-compatible and can be deposited at low temperatures (200°C) without damaging the underlying device. However, localized defects can form during fabrication and compromise the encapsulation. This work investigates a bilayer stack made of a lower PECVD layer (80 nm, 300 nm, or 1000 nm) and an upper ALD layer (100 nm) as a method to mitigate defects. The bilayers were deposited on ~1 cm² aluminum-coated chips and characterized by submerging samples in either aluminum etchant for 215 h to expose pinholes or in 85°C phosphate-buffered saline (PBS) solution for 97 h to assess film degradation. Titanium chips were also fabricated and encapsulated for electrochemical impedance spectroscopy after soaking in 85°C PBS. Chips with ALD–PECVD SiO₂ exhibited a reduced pinhole area and more stable impedance spectra compared to PECVD-only controls, demonstrating superior barrier performance. Interestingly, the thickest encapsulation layers (100 nm ALD/1000 nm PECVD) degraded most rapidly in PBS; further experimentation is needed to identify the causative factors. These results suggest that combining ALD with moderate PECVD thickness may optimize film durability.

Biological materials heal, learn, and adapt thanks to the collective work of tiny agents acting at their microscale. Extensive research in robotics has tried to duplicate this scheme in a synthetic system, yet in their current centimeter‐scale forms, the constituent robots are too large and too few, especially when compared to their biological inspiration. Here, this study shows a new type of high‐stiffness, low‐density material made entirely from robots of submillimeter dimensions. To bear load, these hundred‐micrometer robots directly grow metal onto their bodies and bond together under the control of on‐device microelectronics. The resulting aggregates achieve some of the lowest densities of any material and toughness/elastic moduli approaching the fundamental limits for metallic foams. Going beyond static properties, this study shows that robots can be used to actively repair the material microstructure, restoring stiffness and toughness following compressive fatigue. Broadly, these results clear the way for a new breed of programmable materials with bulk properties that can be rationally tuned over several orders of magnitude through the actions of robots too small to see with the naked eye.

Semiconductor microelectronics are emerging as a powerful tool for building smart, autonomous sub-millimeter robots. Yet a number of existing microrobot platforms, despite significant advantages in speed, robustness, power consumption, or ease of fabrication, have no clear path toward electronics integration, limiting their potential for intelligence. Here, we show how to upgrade a class of self-propelled particles into electronically integrated microrobots, reaping the best of both platforms in a single design. Inspired by electrokinetic micromotors, these robots generate electric fields in a surrounding fluid, and by extension propulsive electrokinetic flows. The underlying physics is captured by a model in which robot speed is proportional to applied current, making design and control straightforward. As proof, we build basic robots at the 100-micron scale that use rudimentary, on-board photovoltaic circuits and a closed-loop optical control scheme to navigate waypoints and move in coordinated swarms at speeds of up to one body length per second. Broadly, the unification of micromotor propulsion with on-robot electronics invites future work to realize robust, fast, easy to manufacture, electronically programmable microrobots that remain operationally viable for months to years.

Semiconductor microelectronics are emerging as a powerful tool for building smart, autonomous robots too small to see with the naked eye. Yet a number of existing microrobot platforms, despite significant advantages in speed, robustness, power consumption, or ease of fabrication, have no clear path towards electronics integration, limiting their intelligence and sophistication when compared to electronic cousins. Here, we show how to upgrade a self-propelled particle into an an electronically integrated microrobot, reaping the best of both in a single design. Inspired by electrokinetic micromotors, these robots generate electric fields in a surrounding fluid, and by extension propulsive electrokinetic flows. The underlying physics is captured by a model in which robot speed is proportional to applied current, making design and control straightforward. As proof, we build basic robots that use on-board circuits and a closed-loop optical control scheme to navigate waypoints and move in coordinated swarms at speeds of up to one body length per second. Broadly, the unification of micromotor propulsion with on-robot electronics clears the way for robust, fast, easy to manufacture, electronically programmable microrobots that operate reliably over months to years.

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10 −12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Abstract: Electronically controllable actuators have shrunk to remarkably small dimensions, thanks to recent advances in materials science. Currently, multiple classes of actuators can operate at the micron scale, be patterned using lithographic techniques, and be driven by complementary metal oxide semiconductor (CMOS)-compatible voltages, enabling new technologies, including digitally controlled micro-cilia, cell-sized origami structures, and autonomous microrobots controlled by onboard semiconductor electronics. This field is poised to grow, as many of these actuator technologies are the firsts of their kind and much of the underlying design space remains unexplored. To help map the current state of the art and set goals for the future, here, we overview existing work and examine how key figures of merit for actuation at the microscale, including force output, response time, power consumption, efficiency, and durability are fundamentally intertwined. In doing so, we find performance limits and tradeoffs for different classes of microactuators based on the coupling mechanism between electrical energy, chemical energy, and mechanical work. These limits both point to future goals for actuator development and signal promising applications for these actuators in sophisticated electronically integrated microrobotic systems.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic learning metamaterials offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here we introduce a nonlinear learning metamaterial -- an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR and nonlinear regression, without a computer. We find our nonlinear learning metamaterial reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

We propose a mechanism for low Reynolds num-ber walking (e.g., legged microscale robots). Whereas locomotion for legged robots has traditionally been classified as dynamic (where inertia plays a role) or static (where the system is always statically stable), we introduce a new locomotion modality we call buoyancy enabled non-inertial dynamic walking in which inertia plays no role, yet the robot is not statically stable. Instead, falling and viscous drag play critical roles. This model assumes squeeze flow forces from fluid interactions combined with a well timed gait as the mechanism by which forward motion can be achieved from a reciprocating legged robot. Using two physical demonstrations of robots with Reynold's number ranging from 0.0001 to 0.02 (a microscale robot in water and a centimeter scale robot in glycerol) we find the model qualitatively describes the motion. This model can help understand microscale locomotion and design new microscale walking robots including controlling forward and backwards motion and potentially steering these robots.

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain neurons and synapses self-adjust based on local information. A man-made self-adjusting (distributed) system capable of performing machine-learning problems would have substantial scaling advantages over typical computational neural networks, in power consumption, speed, and robustness to damage. Furthermore, such a system would allow us to study physical learning without the added complexity of biology. Here we unveil the second-generation design of such a system – a transistor-based self-adjusting analog network that trains itself to perform a wide variety of tasks. Here we demonstrate basic features of the system, including the ability to monitor all internal states. This platform is already faster than a simulation of itself, and is thus an exciting platform for the investigation of physical learning.