Human action recognition (HAR) in videos has garnered widespread attention due to the rich information in RGB videos. Nevertheless, existing methods for extracting deep features from RGB videos face challenges such as information redundancy, susceptibility to noise and high storage costs. To address these issues and fully harness the useful information in videos, we propose a novel heatmap pooling network (HP-Net) for action recognition from videos, which extracts information-rich, robust and concise pooled features of the human body in videos through a feedback pooling module. The extracted pooled features demonstrate obvious performance advantages over the previously obtained pose data and heatmap features from videos. In addition, we design a spatial-motion co-learning module and a text refinement modulation module to integrate the extracted pooled features with other multimodal data, enabling more robust action recognition. Extensive experiments on several benchmarks namely NTU RGB+D 60, NTU RGB+D 120, Toyota-Smarthome and uncrewed aerial vehicles (UAV)-Human consistently verify the effectiveness of our HP-Net, which outperforms the existing human action recognition methods.

With the global ecological environment facing continuous deterioration, effective monitoring of arboreal birds in complex canopy environments remains challenging due to limitations of conventional drones in endurance, size, and habitat disturbance. To address these challenges, this paper presents an ant-inspired micro quadrotor UAV equipped with a lightweight bistable gripper system mimicking the mandibular morphology of leafcutter ants. The design integrates shape memory alloy (SMA)-driven actuation and thermoplastic polyurethane (TPU)-based adaptive grippers, enabling rapid deformation (71 ms switching time) and energy-efficient operation (zero power consumption during perching). Experimental results demonstrate exceptional adaptability in grasping irregular objects (e.g., branches, pen caps) with an 8:1 payload-to-weight ratio. Field tests confirm stable navigation through dense foliage and reliable perching at heights exceeding 5 meters. The system’s compact dimensions (7 cm diameter, 70.5 g weight) and biomimetic approach offer a non-invasive solution for prolonged wildlife observation. This work advances bistable actuator design by combining bio-inspired structural optimization with rapid energy transition principles, showing potential in agile robotics and environmental sensing.

Recently, tactile sensing has attracted great interests in robotics, especially for exploring unstructured objects. Sensor arrays play an important role in the exploration, which generates rich spatio-temporal information. In this work, we propose an efficient tactile recognition model, X-Tacformer. This model pays attention to both spatial and temporal features of tactile sequences from sensor arrays, which is verified by four public datasets, Ev-Objects, Ev-Containers, Augment8000 and BioTac-Dos. Comparative studies show that our model has resulted in a significant improvement of the recognition accuracy by 0.0223, 0.1416, 0.2735 and 0.1592 in these datasets. In order to verify its performances on dataset with rich spatio-temporal features, a self-designed dataset, ALU-Textures, was constructed with 10 fabrics from everyday textiles, aiming to extend the data collection action modes of current datasets by simulating human rubbing movements with the thumb and index fingers of an Allegro hand. Our model also demonstrates efficient salient feature learning capabilities on ALU-Textures, which is further augmented by tactile data augmentation methods.

In-flight high-speed object capturing is crucial in nature to improve survival and adaptation to the environment, such as the predation of frogs, leopards, and eagles. Despite its ubiquitousness in nature, capturing fast-moving objects is extremely challenging in engineering implementations. In this paper, we report an ultrafast gripper based on tunable bistable structures. Different from current designs which are only suitable for objects with certain speed ranges once the grippers are fabricated, the working range of object speed of the proposed gripper could be reprogrammed by controlling the sensitivity of the structures. We present the design and fabrication of the proposed gripper in detail. A theoretical model is introduced to construct the energy landscape of the structures and the force response of the gripper when programmed to different states. The results show that in the original state, the gripper is capable of capturing a flying table tennis ball with a high speed of 15 m/s in only 6 ms. When the proposed gripper is controlled to the ultra-sensitive state, a flying ball with only 1 m/s could also be captured. This work broadens the frontiers of in-flight capturing design, and we envision broader promising applications.

Anti-malware engines report malware labels to detail malice, typically including tags of family, behavior, and platform classes. This capability has been heavily used by the security community to annotate malware families and build reference datasets, which is referred to as crowdsourcing malware family annotation. However, how to associate tags with their corresponding classes in chaotic malware labels (extract class-determined tags) and how to infer ground truth for weakly-tagged samples that hold controversial tags remain open problems. In this paper, we present a novel annotation pipeline to advance further, which includes an incremental parsing scheme and a maximum likelihood estimation scheme. The incremental parsing scheme treats behavior and platform tags as locators and achieves incremental parsing by introducing and iterating the following two algorithms: location first search, which hits family tags using locators, and co-occurrence first search, which finds new locators by family tags. The maximum likelihood estimating scheme models an engine’s ability to identify different families as a confusion matrix and introduces an expectation-maximization algorithm to estimate the matrix, as well as the unknown truth of samples. Experiments across four benchmark datasets indicate that our pipeline outperforms existing work, improving label-level parsing accuracy by an average of 29%, and improving inferring accuracy on weakly-tagged samples by an average of 9%. Our pipeline decouples parsing and inferring, which would pave the way for research on crowdsourcing malware family annotation.

VirusTotal is widely used for malware annotation by providing malware labels from a large set of anti-malware engines. A long-standing challenge in using these inconsistent labels is extracting class-determined tags. In this paper, we present Tagclass,a tool based on incremental parsing to associate tags with their corresponding family, behavior, and platform classes. Tagclasstreats behavior and platform tags as locators and achieves incremental parsing by introducing and iterating the following two algorithms: 1) location first search, which hits family tags using locators, and 2) co-occurrence first search, which finds new locators by family tags. Experiments across two benchmark datasets indicate Tagclassoutperforms existing methods, improving the parsing accuracy by 21% and 28%, respectively. To the best of our knowledge, Tagclassis the first tag class-determined malware label parsing tool, which would pave the way for research on crowdsourcing malware annotation. Tagclasshas been released to the community11https://github.com/crowdma/tagclass.

Soft robotic gloves have shown great advantages in assisting individuals with hand pathologies to perform continuous exercises to restore their hand functions, which could considerably accelerate the rehabilitation process and reduce the costs. However, single rehabilitation mode, difficulty in achieving multiple degrees-of-freedom (DoF) motion, and the lack of high-fidelity feedback still challenge the development of soft robotic gloves. In this paper, we propose a novel design of a robotic glove based on soft-rigid hybrid joint actuators and minimal clutches. We first introduce structures and working principles of the proposed bending joint actuator in detail and then characterize the single joint actuator. Furthermore, we present a performance evaluation of the whole robotic glove in both active and passive modes. Preliminary experimental results showed that (1) in the active training mode, the tested human hand’s muscle effort needed to conduct gross finger flexion increased from 11.16% to 42.60% of the maximum value when the air pressure inside the minimal clutches changed from 0 kPa to 200 kPa; (2) in the passive mode, the 10-DoF robotic glove could assist the tested hand to perform various training exercises and grasp various objects with different hand postures. This paper focuses on the integrated design of multi-DoF structures and variable stiffness mechanisms, which will have an impact on the development of multifunctional soft robots and wearable devices.