Binary mixtures of liquid metal (LM) or low-melting-point alloy (LMPA) in an elastomeric or fluidic carrier medium can exhibit unique combinations of electrical, thermal, and mechanical properties. This emerging class of soft multifunctional composites have potential applications in wearable computing, bio-inspired robotics, and shape-programmable architectures. The dispersion phase can range from dilute droplets to connected networks that support electrical conductivity. In contrast to deterministically patterned LM microfluidics, LMPA- and LM-embedded elastomer (LMEE) composites are statistically homogenous and exhibit effective bulk properties. Eutectic Ga-In (EGaIn) and Ga-In-Sn (Galinstan) alloys are typically used due to their high conductivity, low viscosity, negligible nontoxicity, and ability to wet to nonmetallic materials. Because they are liquid-phase, these alloys can alter the electrical and thermal properties of the composite while preserving the mechanics of the surrounding medium. For composites with LMPA inclusions (e.g., Field's metal, Pb-based solder), mechanical rigidity can be actively tuned with external heating or electrical activation. This progress report, reviews recent experimental and theoretical studies of this emerging class of soft material architectures and identifies current technical challenges and opportunities for further advancement.

Herein, the progress of machine learning methods in the field of soft robotics, specifically in the applications of sensing and control, is outlined. Data‐driven methods such as machine learning are especially suited to systems with governing functions that are unknown, impractical or impossible to represent analytically, or computationally intractable to integrate into real‐world solutions. Function approximation with careful formulation of the machine learning architecture enables the encoding of dynamic behavior and nonlinearities, with the added potential to address hysteresis and nonstationary behavior. Supervised learning and reinforcement learning in simulation and on a wide variety of physical robotic systems have shown promising results for the use of empirical data‐driven methods as a solution to contemporary soft robotics problems.

The integration of synthetic biology and soft robotics can fundamentally advance sensory, diagnostic, and therapeutic functionality of bioinspired machines. However, such integration is currently impeded by the lack of soft-matter architectures that interface synthetic cells with electronics and actuators for controlled stimulation and response during robotic operation. Here, we synthesized a soft gripper that uses engineered bacteria for detecting chemicals in the environment, a flexible light-emitting diode (LED) circuit for converting biological to electronic signals, and soft pneu-net actuators for converting the electronic signals to movement of the gripper. We show that the hybrid bio-LED-actuator module enabled the gripper to detect chemical signals by applying pressure and releasing the contents of a chemical-infused hydrogel. The biohybrid gripper used chemical sensing and feedback to make actionable decisions during a pick-and-place operation. This work opens previously unidentified avenues in soft materials, synthetic biology, and integrated interfacial robotic systems.

Recent progress in soft-matter sensors has shown improved fabrication techniques, resolution, and range. However, scaling up these sensors into an information-rich tactile skin remains largely limited by designs that require a corresponding increase in the number of wires to support each new sensing node. To address this, a soft tactile skin that can estimate force and localize contact over a continuous 15 mm 2 area with a single integrated circuit and four output wires is introduced. The skin is composed of silicone elastomer loaded with randomly distributed magnetic microparticles. Upon deformation, the magnetic particles change position and orientation with respect to an embedded magnetometer, resulting in a change in the net measured magnetic field. Two experiments are reported to calibrate and estimate both location and force of surface contact. The classification algorithms can localize pressure with an accuracy of >98% on both grid and circle pattern. Regression algorithms can localize pressure to a 3 mm 2 area on average. This proof-of-concept sensing skin addresses the increasing need for a simple-to-fabricate, quick-to-integrate, and information-rich tactile surface for use in robotic manipulation, soft systems, and biomonitoring.

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