Flesh encodes a variety of haptic information including deformation, temperature, vibration, and damage stimuli using a multisensory array of mechanoreceptors distributed on the surface of the human body. Currently, soft sensors are capable of detecting some haptic stimuli, but whole-body multimodal perception at scales similar to a human adult (surface area ~17,000 square centimeters) is still a challenge in artificially intelligent agents due to the lack of encoding. This encoding is needed to reduce the wiring required to send the vast amount of information transmitted to the processor. We created a robotic flesh that could be further developed for use in these agents. This engineered flesh is an optical, elastomeric matrix “innervated” with stretchable lightguides that encodes haptic stimuli into light: temperature into wavelength due to thermochromic dyes and forces into intensity due to mechanical deformation. By exploiting the optical properties of the constitutive materials and using machine learning, we infer spatiotemporal, haptic information from light that is read by an image sensor. We demonstrate the capabilities of our system in various assemblies to estimate temperature, contact location, normal and shear force, gestures, and damage from temporal snapshots of light coming from the entire haptic sensor with errors <5%.

An acoustic liquefaction approach to enhance the flow of yield stress fluids during Digital Light Processing (DLP)‐based 3D printing is reported. This enhanced flow enables processing of ultrahigh‐viscosity resins (μapp > 3700 Pa s at shear rates  = 0.01 s–1) based on silica particles in a silicone photopolymer. Numerical simulations of the acousto–mechanical coupling in the DLP resin feed system at different agitation frequencies predict local resin flow velocities exceeding 100 mm s–1 at acoustic transduction frequencies of 110 s–1. Under these conditions, highly loaded particle suspensions (weight fractions, ϕ = 0.23) can be printed successfully in complex geometries. Such mechanically reinforced composites possess a tensile toughness 2000% greater than the neat photopolymer. Beyond an increase in processible viscosities, acoustophoretic liquefaction DLP (AL‐DLP) creates a transient reduction in apparent viscosity that promotes resin recirculation and decreases viscous adhesion. As a result, acoustophoretic liquefaction Digital Light Processing (AL‐DLP) improves the printed feature resolution by more than 25%, increases printable object sizes by over 50 times, and can build parts >3 × faster when compared to conventional methodologies.

Conventional strain gauges are not designed for accurate measurement over the large range of deformations possible in compliant textiles. The thin, lightweight, and flexible nature of textiles also makes it challenging to attach strain gauges in a way that does not affect the mechanical properties. In this manuscript, soft, highly extensible fibers that propagate light (i.e., stretchable lightguides) are stitched as a strain gauge to map the deformation of a nylon parachute textile under tension. When under load, these fiber optic strain gauges propagate less light, and this strain‐induced light modulation is used to accurately (absolute error≈2.93%; Std. Dev.: 3.02%) measure strain in the <30% range before these textiles fail. This system has directionality; strain in parallel to the sensor results in little light attenuation while perpendicular loading shows high sensitivity (Gauge factor⊥≈24.8 and Gauge factor||≈0.05 at the first 1% strain). Structural and optical simulations are coupled to demonstrate that load transfer on the fiber optic by the stitchwork is the dominating cause of signal modulation. To further validate the hypotheses, digital image correlation was used under dynamic loading conditions to show that these sensors do not significantly affect the mechanical properties.

We present formulation and open-source tools to achieve in-material model predictive control of sensor/actuator systems using learned forward kinematics and on-device computation. Microcontroller units that compute the prediction and control task while colocated with the sensors and actuators enable in-material untethered behaviors. In this approach, small parameter size neural network models learn forward kinematics offline. Our open-source compiler, nn4mc, generates code to offload these predictions onto MCUs. A Newton-Raphson solver then computes the control input in real time. We first benchmark this nonlinear control approach against a PID controller on a mass-spring-damper simulation. We then study experimental results on two experimental rigs with different sensing, actuation and computational hardware: a tendon-based platform with embedded LightLace sensors and a HASEL-based platform with magnetic sensors. Experimental results indicate effective high-bandwidth tracking of reference paths (≥120 Hz) with a small memory footprint (≤6.4% of flash memory). The measured path following error does not exceed 2mm in the tendon-based platform. The simulated path following error does not exceed 1mm in the HASEL-based platform. The mean power consumption of this approach in an ARM Cortex-M4f device is 45.4 mW. This control approach is also compatible with Tensorflow Lite models and equivalent on-device code. In-material intelligence enables a new class of composites that infuse autonomy into structures and systems with refined artificial proprioception.

Aeronautics research has continually sought to achieve the adaptability and morphing performance of avian wings, but in practice, wings of all scales continue to use the same hinged control-surface embodiment. Recent research into compliant and bio-inspired mechanisms for morphing wings and control surfaces has indicated promising results, though often these are mechanically complex, or limited in the number of degrees-of-freedom (DOF) they can control. Seeking to improve on these limitations, we apply a new paradigm denoted Autonomous Material Composites to the design of avian-scale morphing wings. With this methodology, we reduce the need for complex actuation and mechanisms, and allow for three-dimensional placement of stretchable fiber optic strain gauges (Optical Lace) throughout the metamaterial structure. This structure centers around elastomeric conformal lattices, and by applying functionally-graded warping and thickening to this lattice, we allow for local tailoring of the compliance properties to fit the desired morphing. As a result, the wing achieves high-deformation morphing in three DOF: twist, camber, and extension/compression, with these morphed shapes effectively modifying the aerodynamic performance of the wing, as demonstrated in low-Reynolds wind tunnel testing. Our sensors also successfully demonstrate differentiable trends across all degrees of morphing, enabling the future state estimation and control of this wing.