Gait-based emotion recognition has received considerable attention due to its non-invasive capturing manner. However, most existing works learn the gait representations by treating different classes independently, which ignores the inherent class ambiguity in this field. Therefore, we propose a Multi-soft-label Guided Supervised Contrastive Learning (MSL-SCL) framework, which leverages the class correlation information in soft labels to explicitly guide the SCL, thereby alleviating the ambiguous gait representation. Specifically, a Soft-Label SCL (Sof-SCL) module is designed to select positive and negative samples based on their soft-label similarity to the anchors, and the similarity is further incorporated into a novel contrastive loss function for the refinement. Moreover, a Prior-Guided SCL (Prior-SCL) is introduced to capture the subtle changes in gait and employed as soft labels to provide an adaptive supervision for SCL. Extensive experimental results on the Emotion-Gait dataset demonstrate that our method outperforms SOTAs with a mean average precision of 89.8%.

In this work, we propose a novel quadrotor design capable of folding its arms vertically to grasp objects and navigate through narrow spaces. The transformation is controlled actively by a central servomotor, gears, and racks. The arms connect the motor bases to the central frame, forming a parallelogram structure that ensures the propellers maintain a constant orientation during morphing. In its stretched state, the quadrotor resembles a conventional design, and when contracted, it functions as a gripper with grasping components emerging from the motor bases. To mitigate disturbances during transforming and grasping payloads, we employ an adaptive sliding mode controller with a disturbance observer. After fully folded, the quadrotor frame shrinks to 67% of its original size. The control performance and versatility of the morphing quadrotor are validated through real-world experiments.

This paper presents a novel biomimetic morphing quadrotor design inspired by the morphology of an eagle claw during prey capture. The arms of the quadrotor are capable of vertical folding to enable dynamic grasping, mimicking the transition of the eagle claw from an open to a closed state. This transition is achieved through the rotation of a central servomotor and the associated movement of 20 links. Thanks to the closed-loop multi-link structure of the frame, the propellers of the quadrotor remain in a fixed orientation when the arms are folded, allowing for system stabilization at any arm rotation angle. The geometric property of the whole frame is analyzed to determine the relationships and constraints of the links, which is important in experimental vehicle fabrication. To handle possible physical property changes and external disturbances during grasping, the adaptive sliding mode controllers are applied. To deal with objects of unknown size in grasping tasks, an admittance filter is proposed for adaptive morphology. While in flight, our proposed morphing quadrotor is able to rapidly or continuously transition to any configuration within its range smoothly. Experimental results show the ability of the quadrotor to dynamically grasp various unknown objects at 0.4m/s without additional tools, as well as its versatility in traversal of narrow spaces and perching.

Traditional methods based on Lyapunov analysis and learning-based approaches such as reinforcement learning (RL) are two powerful tools in visual servo tasks. Traditional methods are interpretable and their stability can be guar-anteed by Lyapunov analysis. However, they tend to have a high dependency on an accurate system dynamic model. RL approaches learn to act based on past experiences and thus have higher adaptability on disturbances and errors. However, the training process is long and its safety or stability is generally hard to guarantee, making real-world training risky. In this paper, we propose a residual RL framework for training a multicopter to finish visual servo tasks under disturbances, guided by the system safety in terms of system Lyapunov function. Such an approach compensates for the lack of disturbance-rejection ability of the traditional method, and optimizes stability explicitly so the RL agent makes safer actions both during the training and in the final policy. A comparison between our approach and the baselines is provided in simulation, and real-world experiments on a multicopter are also carried out to show our effectiveness. We believe that this work moves one step toward achieving RL applications on real-world robotic systems.

Contact-based aerial interaction control in unknown environments is a challenging issue. To solve this problem, this paper presents an image-based impedance control scheme for an aerial manipulator. Unlike points and image moments, the line features considered in this paper cannot uniquely determine the pose of the onboard camera. Therefore, a nonlinear observer is proposed to online provide supplementary 3D information of the system. After that, a hierarchical tracking controller, which is equipped with an image-space impedance filter, is designed to implement a trajectory tracking task with tunable compliance, while at the same time achieving a subtask that aims to determine the final pose of the aerial manipulator. By planning task-space trajectories, desired interaction behavior can be specified without relying on the global position of the system. Furthermore, the effectiveness criterion of the observer is derived, and the stability of the system is proven. Several experiments, including a push-and-slide task, robustness analysis, and a comparison with the state of the art have been conducted to demonstrate the feasibility of the method. Note to Practitioners—This article presents a method for controlling an aerial manipulator to interact with unknown environments without measuring the global position of both the environment and the aerial manipulator. Furthermore, unlike the existing methods, the proposed visual servoing method is based on line features, which are generally more robust than point features. Moreover, it would be intuitive for users to specify the approaching path and the interaction behavior of the aerial manipulator by just planning the image feature trajectories and the desired distance between the aerial manipulator and the environment. This distance is online estimated using the visual feedback of a monocular camera, instead of relying on any costly 3D-vision sensors or lidars, making it an economical solution for aerial manipulation in field environments.

In this paper, an image-based impedance control strategy for force tracking of an unmanned aerial manipulator (UAM) is presented. Firstly, image features with nice decoupling characteristics are designed and the relationship between the camera motion and the image features is derived. Then, a two-stage strategy is proposed to achieve force tracking of the UAM on a planar object in an arbitrary pose. The first stage drives the end-effector perpendicular to the object’s planer surface by pure visual servoing. To achieve force tracking under the visual guidance, an adaptive visual impedance control method which adjusts the target stiffness according to the force tracking error and the visual feature error is proposed in the second stage. The closed-loop system is proved asymptotically stable by means of Lyapunov analysis. Further, the stability in free flight phase of the stage two is also analyzed and ensured. Finally, experiments were carried out including a whiteboard cleaning task in different poses. The experimental results illustrate the validity and effectiveness of the proposed approach. Note to Practitioners—This work is motivated by the contact force tracking problem of an unmanned aerial manipulator (UAM) without the position measurement. In recent years, impedance control is widely used in force tracking problems. However, position measurement is needed both for the robot and the object in most studies. To generate desired force under visual guidance, image-based visual servoing is combined with a novel impedance controller with variable stiffness. The idea is intuitive. For a human in contact with a wall, the contact force is controlled by adjusting his arm stiffness, which means the stiffness is adapted to the difference between the desired and actual contact force. Furthermore, the stiffness is also adapted to the visual tracking error, removing the assumption that no tracking error is in the inner loop of the impedance controller in the previous works. The proposed strategy is a promising solution for real applications and is validated by a board cleaning experiment in this paper.

Physical human–robot interaction in the field of aerial vehicles has received more research attention in recent years. In this article, a visual impedance control strategy for human–aerial robot cooperative transportation with a tethered vehicle is presented. Without a positioning system, the aerial vehicle is controlled to follow the human partner by using cable force and visual features of the object as feedback. Furthermore, being aware of human motion is important to improve efficiency and smoothness of the cooperation. Without measuring velocities of the aerial vehicle and the human, we propose to directly estimate the relative velocity of them by a vision-based velocity observer. This estimated velocity is then integrated into a visual impedance scheme. The stability of the system is rigorously proved by Lyapunov analysis and passivity analysis. Indoor experiments where a human participant transports a long bar with a tethered aerial vehicle are conducted. Results of experiments under different human velocities and intentions demonstrate the effectiveness and reliability of the proposed method.

We aim to further explore the transfer of spatial knowledge from virtual to real spaces. Based on previous research on spatial memory in immersive virtual reality (VR) we ran a study that looked at the effect of three locomotion techniques (joystick, pointing-and-teleporting and walking-in-place) on object location learning and recall. Participants were asked to learn the location of a virtual object in a virtual environment (VE). After a short period of time they were asked to recall the location by placing a real version of the object in the real-world equivalent environment. Results indicate that the average placement error, or distance between original and recalled object location, is approximately 20cm for all locomotion technique conditions. This result is similar to the outcome of a previous study on spatial memory in VEs that used real walking. We report this unexpected finding and suggest further work on spatial memory in VR by recommending the replication of this study in different environments and using objects with a wider diversity of properties, including varying sizes and shapes.