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Soft robotics is an emerging technology that provides robots with the ability to adapt to the environment and safely interact with it. Here, the ability of these robots to identify the surface of interaction is critical for grasping and locomotion tasks. This paper describes the capability of a four-limb soft robot that can identify background materials through the collection of reflection coefficients using an embedded antenna and machine learning techniques. The material of a soft-limb robot was characterized in terms of the relative permittivity and the loss tangent for the design of an antenna to collect reflection coefficients. A slot antenna was designed and embedded into a soft limb in order to extract five features in reflection coefficients including the resonant frequency, −3 dB bandwidth taken from the lowest S11, the value of the lowest S11, −3 dB bandwidth taken from the highest S11, and the number of resonant frequencies. A soft robot with the embedded antenna was tested on nine different background materials in an attempt to identify surrounding terrain information and a better robotic operation. The tested background materials included concrete, fabric, grass, gravel, metal, mulch, soil, water, and wood. The results showed that the robot was capable of distinguishing among the nine different materials with an average accuracy of 93.3% for the nine background materials using a bagged decision-tree-based ensemble method algorithm. 

Abstract Nanoscopic packing structures crucially determine the charge conduction and the consequent functionalities of organic semiconductors including bulk heterojunctions (BHJs), which are dependent on various processing parameters. Today's high‐performance colloidal quantum dot photovoltaics (CQDPVs) employ functional organic semiconductors as a hole transport layer (HTL). However, the processing of those films replicates a protocol dedicated to high‐performance organic PVs, and thus little is known about how to control the molecular packing structures to maximize the hole extraction function of the HTLs. Herein, it is uncovered that the random‐oriented, but closer‐packed BHJ crystallites, constructed by 1,2‐dichlorobenzene (o‐DCB) as a solvent, allow exceptional charge conduction vertically across the film and restrict diffusion‐driven charge transfer process, enabling ultrafast hole funneling from CQD to BHJ to be extracted. As a result, a power conversion efficiency of 13.66% with high photocurrent >34 mA cm⁻²is achieved by employingo‐DCB‐processed BHJ HTL, far exceeding the performance of the CQDPV solely employing neat polymer HTL. A charge conduction mechanism associated with the BHJ HTL structure suppressing the bimolecular recombination is proposed. This works not only suggests key principles to control the packing structures of organic HTLs but also opens a new avenue to boost optoelectronic performance.