ABSTRACT The ability to generate facial expressions is essential for humanoid social robots to engage in natural, human‐like interactions. This capability significantly enhances the fluidity of human‐robot communication and the precision of emotional expression. However, current methods rely heavily on pre‐programmed behavioral patterns, which are manually implemented at considerable cost in both time and human labor. To enable humanoid robots to autonomously acquire generalized expressive capabilities, they must learn human‐like expressions via self‐supervised training. To address this challenge, we present a highly biomimetic robotic face equipped with physically actuated electronic facial units, alongside an end‐to‐end learning framework that integrates Kolmogorov‐Arnold Networks (KAN) with attention mechanisms. In contrast to previous approaches, we have also developed an automated data collection system guided by expert‐designed facial motion primitives, enabling the construction of a high‐quality dataset. Notably, to the best of our knowledge, this constitutes the first facial expression dataset specifically designed for humanoid social robots. Extensive evaluations demonstrate that our method enables accurate and diverse facial mimicry across a range of test subjects.

Robotic feeding and serving require accurate reasoning about deformable foods, including recognizing food type and estimating the scooped weight. This is challenging because visually similar foods (e.g., millet vs. rice) provide ambiguous appearance cues, while force responses during scooping vary nonlinearly with portion size and utensil dynamics. We propose the Force-guided Text-prompt Multimodal Transformer (FTMT), a framework that integrates vision, force, and language supervision for robust food property estimation. Text prompts are used during training to semantically regularize force features, guiding multimodal tokenization and bi-directional cross-attention between visual and force representations. Experiments on eight deformable food types demonstrate that FTMT achieves improved recognition accuracy and lower weight estimation error compared to force-based and multimodal fusion baselines. Ablation studies highlight the importance of text–force alignment, force preprocessing, and cross-attention fusion. By enabling robots to reliably identify food and predict scooped weight, FTMT represents a step toward adaptive and preference-aware robot-assisted feeding in real-world settings.

This paper proposes FABG (Facial Affective Behavior Generation), an end-to-end imitation learning system for human-robot interaction, designed to generate natural and fluid facial affective behaviors. In interaction, effectively obtaining high-quality demonstrations remains a challenge. In this work, we develop an immersive virtual reality (VR) demonstration system that allows operators to perceive stereoscopic environments. This system ensures that "the operator’s visual perception matches the robot’s sensory input" and "the operator’s actions directly determine the robot’s behaviors" - as if the operator replaces the robot in human interaction engagements. We propose a prediction-driven latency compensation strategy to reduce robotic reaction delays and enhance interaction fluency. FABG naturally acquires human interactive behaviors and subconscious motions driven by intuition, eliminating manual behavior scripting. We deploy FABG on a real-world 25 degree-of-freedom (DoF) humanoid robot, validating its effectiveness through four fundamental interaction tasks: affective interaction, dynamic tracking, foveated attention, and gesture recognition, supported by data collection and policy training.

The facial expression generation capability of humanoid social robots is critical for achieving natural and human-like interactions, playing a vital role in enhancing the fluidity of human-robot interactions and the accuracy of emotional expression. Currently, facial expression generation in humanoid social robots still relies on pre-programmed be-havioral patterns, which are manually coded at high human and time costs. To enable humanoid robots to autonomously acquire generalized expressive capabilities, they need to develop the ability to learn human-like expressions through self-training. To address this challenge, we have designed a highly biomimetic robotic face with physical-electronic animated facial units and developed an end-to-end learning framework based on KAN (Kolmogorov-Arnold Network) and attention mechanisms. Unlike previous humanoid social robots, we have also meticulously designed an automated data collection system based on expert strategies of facial motion primitives to construct the dataset. Notably, to the best of our knowledge, this is the first open-source facial dataset for humanoid social robots. Comprehensive evaluations indicate that our approach achieves accurate and diverse facial mimicry across different test subjects.

Weakly supervised referring expression grounding aims to train a model without the manual labels between image regions and referring expressions during the training phase. Current predominant models often adopt deep structures to reconstruct the region-expression correspondence. A crucial deficiency of the existing approaches lies in that these models neglect to exploit potential valuable information to further improve their grounding performance. To address this issue, we leverage knowledge distillation as a unique scheme to excavate and transfer helpful information for acquiring a better model. Specifically, we propose a target-guided knowledge distillation framework that accounts for region-expression pairs reconstruction and matching. We reactivate the target-related prediction information learned by a pre-trained teacher model and transfer the target-related prediction knowledge from the teacher to guide the training process and boost the performance of the student model. We conduct extensive experiments on three benchmark datasets, i.e., RefCOCO, RefCOCO+, and RefCOCOg. Without bells and whistles, our approach achieves state-of-the-art results on several splits of benchmark datasets. The implementation codes and trained models are available at: https://github.com/dami23/WREG_KD.

This article studies the tracking control of a quadrotor unmanned aerial vehicle (UAV) under time-varying external disturbances. An event-triggered sliding mode control (SMC) strategy is proposed by introducing a new triggering condition form of desired trajectory, quadrotor position, and velocity. In the sense of Lyapunov theory, the stability of the entire closed-loop control system is analyzed, and it is proved that the tracking error is adjusted to an adjustable set around zero. We show that the Zeno phenomenon can be avoided; that is, a positive minimum inter-event time is assured. One of the salient features of the proposed strategy is that it can reduce the update frequency of the control efforts, thereby ensuring desirable tracking performance under limited communication bandwidth. Comparative simulation and experimental results are provided to show the efficacy of our framework.

Event-triggered formation control of linear continuous-time multiagent systems is studied in this article. A complex-valued Laplacian is adopted by the local information of desired formation. For each agent, an event-triggering mechanism based on the neighboring information at event-triggering time instants is presented and continuous communications between neighboring agents are avoided. Furthermore, an event-triggered control strategy using the idea of dynamic state observer is designed. It is shown that any desired formation shape can be achieved. Moreover, the Zeno-behavior is strictly excluded. Finally, effectiveness of the obtained theoretical results is validated by two simulation examples.

The ability to adapt their perception to changing environments is a core characterization of intelligent robots. At present, Unsupervised Domain Adaptation (UDA) methods are used to address this problem where the adaptation task is formulated as a transfer problem from a well-described scenario (source domain) to a new scenario (target domain). In order to implement the domain adaptation, these methods require access to the source data for achieving the distribution matching between both domains. However, in many real-world applications, the source data is inaccessible and only a source model pre-trained on the source domain is available during the transfer process. Therefore, the traditional UDA methods cannot support the challenging setting. This paper developed a new hypothesis transfer method to achieve model adaptation with gradual knowledge distillation. Specifically, we first prepare a source model through training a deep network on the labeled source domain by supervised learning. Then, we transfer the source model to the unlabeled target domain by self-training. To implement gradual knowledge distillation, we sliced the self-training into several epochs and then used the soft pseudo-labels from the latest epoch to guide the current epoch. In this process, the soft labels were generated by a semantic fusion on a proposed geometry of the neighborhood. To regulate the self-training, we developed a new objective constructed on the neighborhood. Experiments on three benchmarks have confirmed the state-of-the-art results of our method.

Currently, most table tennis robots concentrate on the canonical position control problem while ignoring the actual velocity control requirements. In this paper, we consider these requirements and propose a new table tennis robot framework. First, a tailor-made mechanical structure is designed such that the robot can reach large workspaces. Thereafter, in the table tennis trajectory prediction process, a clustering algorithm is introduced to screen the heterogeneous predicted hitting points and filter the invalid ones, thereby significantly improving the prediction accuracy. By using quintic polynomial trajectory planning, smooth and stable high-speed control of the robot hitting motion can be obtained. Finally, a position-based strategy and a velocity-based strategy are devised for returning the table tennis. Extensive experiments demonstrate that the accuracy of the ball's trajectory prediction algorithm is more than 92%. The success rate of returning the ball exceeds 95% at the ball velocity of 3-7 m/s, and the velocity-based strategy performs better compared with the position-based approach at the ball velocity of 7-9 m/s.

Domain adaptation methods are used to address a problem, in which train scenario (source domain) and test scenario (target domain) are different. The existing methods mainly perform adaptation via reducing domain discrepancy from the view of a probability distribution. However, the idea of probability distribution matching always leads to a complex optimization process. Thereby these methods are difficult to apply in some scenario like online application or fast perception in dynamic environments. In this paper, we propose a new and simple domain adaptation method that utilizes confidence- samples to facilitate the classifier training on the target domain. Here, the confidence-samples are a subset of the target samples, and they have very credibly predicted labels. In order to detect the samples, a Category Similarity Collaborative Representation (CSCR) is first developed, by which the raw labels of all target samples are predicted using the smallest projection error according to the law of category. After this, the confidence score of the raw predicted labels is evaluated by the energy context information of CSCR. Finally, the target samples with a high confidence score are selected. Because of the linearity of CSCR, our method avoids complex optimization for matching the probability distribution. Empirical studies on a standard dataset demonstrate the advantages of our method.

This paper presents a simple 2D passive dynamic walking model with straight legs based on a novel hip joint, called T-joint. The model directly solves the common foot-scuffing problem in straight-legged walkers without introducing any new degree of freedom or additional motion phase, which is unavoidable in kneed robots. The mathematic model of the new walker is a 4D hybrid dynamical system, which can be reduced to a 3D Poincaré map. A stable period-1 walking gait is found by searching the map with a proposed algorithm. Numerical studies show that the model performs well for disturbances on initial conditions and also on a large slope. The stable walking gait is verified by a virtual prototype in ADAMS. This study has been successfully used to make an efficient walking robot called Xingzhe, which has set a new world record for the longest distance after 134km non-stoping walking.