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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.more » « less
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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 mm2area 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 mm2area 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.
