Fleets of autonomous robots have been deployed for exploration of unknown scenes for features of interest, e.g., subterranean exploration, reconnaissance, search and rescue missions. During exploration, the robots may encounter un-identified targets, blocked passages, interactive objects, temporary failure, or other unexpected events, all of which require consistent human assistance with reliable communication for a time period. This however can be particularly challenging if the communication among the robots is severely restricted to only close-range exchange via ad-hoc networks, especially in extreme environments like caves and underground tunnels. This paper presents a novel human-centric interactive exploration and assistance framework called FlyKites, for multi-robot systems under limited communication. It consists of three interleaved components: (I) the distributed exploration and intermittent communication (called the “spread mode”), where the robots collaboratively explore the environment and exchange local data among the fleet and with the operator; (II) the simultaneous optimization of the relay topology, the operator path, and the assignment of robots to relay roles (called the”relay mode”), such that all requested assistance can be provided with minimum delay; (III) the human-in-the-loop online execution, where the robots switch between different roles and interact with the operator adaptively. Extensive human-in-the-loop simulations and hardware experiments are performed over numerous challenging scenes.

Micro-expression recognition (MER) holds significance in uncovering hidden emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a lowdimensional and compact modality, which achieves lower computational cost and potentially concentrates on ME-related movement features. However, the discriminability of facial landmarks for MER is unclear. Thus, this paper investigates the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs with facial landmarks. Firstly, a geometric twostream graph network is constructed to aggregate the low-order and high-order geometric movement information from facial landmarks to obtain discriminative ME representation. Secondly, a self-learning fashion is introduced to automatically model the dynamic relationship between nodes even long-distance nodes. Furthermore, an adaptive action unit loss is proposed to reasonably build a strong correlation between landmarks, facial action units and MEs. Notably, this work provides a novel idea with much higher efficiency to promote MER, only utilizing graphbased geometric features. The experimental results demonstrate that the proposed method achieves competitive performance with a significantly reduced computational cost. Furthermore, facial landmarks significantly contribute to MER and are worth further study for high-efficient ME analysis.

Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in various fields. Early methods for Micro-expression Recognition (MER) are mainly based on traditional features. Recently, with the success of Deep Learning (DL) in various tasks, neural networks have received increasing interest in MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid facial movements, leading to difficult data collection and annotation, thus publicly available datasets are usually small-scale. Currently, various DL approaches have been proposed to solve the ME issues and improve MER performance. In this survey, we provide a comprehensive review of deep MER and define a new taxonomy for the field encompassing all aspects of MER based on DL, including datasets, each step of the deep MER pipeline, and performance comparisons of the most influential methods. The basic approaches and advanced developments are summarized and discussed for each aspect. Additionally, we conclude the remaining challenges and potential directions for the design of robust MER systems. Finally, ethical considerations in MER are discussed. To the best of our knowledge, this is the first survey of deep MER methods, and this survey can serve as a reference point for future MER research.