Nature has proved that multiple sense and processing capabilities are critical for target recognition and race survival. As such, advanced robots that perform missions in an unstructured environment highly need organism‐similar multimodal sensing and processing systems for sophisticated unstructured environmental stimuli. Herein, recent progress in multimodal sensing and processing systems for advanced robotics is reviewed. Multimodal sensors including tactile sensors that capture surface properties of objects (i.e., thermal conductivity, temperature, softness, and electron affinity), visual sensors that capture the color and size of objects, and gas sensors that capture object smell are summarized. The multimodal data fusion algorithms that process multimodal signals from multimodal sensors to achieve object recognition and decision making are also presented. The challenges and future development of multimodal sensors and data fusion algorithms are further discussed. Advances in these areas open new avenues for advanced robotics applications in human–robot collaboration, rescue missions, garbage sorting, and intelligent prosthetics.

Piezoelectric sensors with high performance and low-to-zero power consumption meet the growing demand in the flexible microelectronic system with small size and low power consumption, which are promising in robotics and prosthetics, wearable devices and electronic skin. In this review, the development process, application scenarios and typical cases are discussed. In addition, several strategies to improve the performance of piezoelectric sensors are summed up: (1) material innovation: from piezoelectric semiconductor materials, inorganic piezoceramic materials, organic piezoelectric polymer, nanocomposite materials, to emerging and promising molecular ferroelectric materials. (2) designing microstructures on the surface of the piezoelectric materials to enlarge the contact area of piezoelectric materials under the applied force. (3) addition of dopants such as chemical elements and graphene in conventional piezoelectric materials. (4) developing piezoelectric transistors based on piezotronic effect. In addition, the principle, advantages, disadvantages and challenges of every strategy are discussed. Apart from that, the prospects and directions of piezoelectric sensors are predicted. In the future, the electronic sensors need to be embedded in the microelectronic systems to play the full part. Therefore, a strategy based on peripheral circuits to improve the performance of piezoelectric sensors is proposed in the final part of this review.

Self‐powered flexible tactile sensors based on triboelectric nanogenerators (TENGs) can be of use in the development of robotic intellisense and interaction. Such sensors typically use triboelectronegative material as top layer, requiring contacting and separating with specific interface material to operate, and may result in suboptimal performance under practical conditions. Herein, a self‐powered interface‐independent tactile sensor array that is based on bilayer single‐electrode TENGs is reported. By integrating both triboelectronegative and triboelectropositive layers in the structure, the sensor overcomes the material restriction of top layer and could sense applied pressure from any material. Furthermore, a 5 × 5 sensor array is fabricated to realize the detection of contact point and the recognition of trajectory. Last, the sensor array is successfully implemented as electronic skin (e‐skin) in a robotic hand for tactile sensing and human–machine interaction. In this regard, it can be envisioned that such tactile sensors possess a promising application in intelligent robots including robotic e‐skin and artificial intelligence.

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature‐pressure electronic skin with decoupling capability (TPD e‐skin), inspired by the high‐low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual‐state amplifying microstructure and contact resistance modulation, the MXene TPD e‐skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high‐low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature‐pressure signals into three types of nerve‐acceptable frequency signals, corresponding to thermoreceptors, slow‐adapting receptors, and fast‐adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain‐like machine learning‐based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality‐decoupling sensing and deep neural integration.