Indoor semantic segmentation is fundamental to computer vision and robotics, supporting applications such as autonomous navigation, augmented reality, and smart environments. Although RGB-D fusion leverages complementary appearance and geometric cues, existing methods often depend on computationally intensive cross-attention mechanisms and insufficiently model intra- and inter-modal feature relationships, resulting in imprecise feature alignment and limited discriminative representation. To address these challenges, we propose DiffPixelFormer, a differential pixel-aware Transformer for RGB-D indoor scene segmentation that simultaneously enhances intra-modal representations and models inter-modal interactions. At its core, the Intra-Inter Modal Interaction Block (IIMIB) captures intra-modal long-range dependencies via self-attention and models inter-modal interactions with the Differential–Shared Inter-Modal (DSIM) module to disentangle modality-specific and shared cues, enabling fine-grained, pixel-level cross-modal alignment. Furthermore, a dynamic fusion strategy balances modality contributions and fully exploits RGB-D information according to scene characteristics. Extensive experiments on the SUN RGB-D and NYUDv2 benchmarks demonstrate that DiffPixelFormer-L achieves mIoU scores of 54.28% and 59.95%, outperforming DFormer-L by 1.78% and 2.75%, respectively. Moreover, its effectiveness is further validated on the large-scale ScanNetv2 dataset, indicating strong generalization capability. Code is available at https://github.com/gongyan1/DiffPixelFormer.

Traditional pedestrian detection methods based on red-green-blue (RGB) images struggle in adverse illumination, but a key capability required for pedestrian detection is all-day detection due to its critical role in diverse applications, e.g., security, surveillance, and autonomous driving. To address this issue, multispectral pedestrian detection attempts to introduce thermal images to supplement the RGB images, since they can be captured based on heat radiation difference without relying on external light sources. However, how to fuse the two modalities effectively is still lacking in-depth investigation. To prompt this field, we propose an implicit illumination-aware representation to address the limited availability of specific illumination labels in existing multispectral datasets, coupled with a prefusion feature alignment strategy to reconcile spatial misalignments of identical objects across modalities. We also identify four critical fusion challenges, revealing persistent limitations in existing multispectral detectors' ability to holistically address these issues, particularly regarding underdeveloped cross-modal interactions and suboptimal cross-domain feature fusion. To this end, we propose a universal multispectral pedestrian detection paradigm (UMPDP), which includes a modality alignment module (MAM) for adaptive feature space alignment, a differential modality fusion module (DMFM) to enhance the relationship of different modalities, and a task-conditioned illumination module (TCIM) to dynamically adjust network weights based on illumination condition. Extensive experiments on KAIST and CVC-14 datasets demonstrate the general effectiveness of our proposed method. Code is available at https://github.com/gongyan1/UMPDP.

Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we propose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET framework could speed up the exploration process and acquire more satisfying optimized results for naive wearers.

Humans use tools to complete impact-aware tasks such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a crucial role. The underlying motion planning challenge consists of grabbing the tool in preparation for the use of this tool with an optimal body posture. Directional manipulability describes the dexterity of force and velocity in a joint configuration along a specific direction. In order to take directional manipulability and tool affordances into account, we apply an optimal control method combining iterative linear quadratic regulator (iLQR) with the alternating direction method of multipliers (ADMM). Our approach considers the notion of tool affordances to solve motion planning problems, by introducing a cost based on directional velocity manipulability. The proposed approach is applied to impact tasks in simulation and on a real 7-axis robot, specifically in a nail-hammering task with the assistance of a pilot hole. Our comparison study demonstrates the importance of maximizing directional manipulability in impact-aware tasks.

Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.

Pathological tremor is defined as a roughly sinusoidal movement and usually occurs in the upper limb impacting individuals activities of daily livings. Functional electrical stimulation (FES) is proposed as a potential alternative for cancelling the pathological tremor. However, the feasibility and accuracy of FES depends on the estimation of amplitude and frequency of tremor signals measured by sensors. In this study, a novel algorithm incorporating a sliding fast Fourier transform (SFFT), an interpolation procedure and a limitation module of frequency range is developed to estimate tremor frequency and separate the tremor components from raw data. Based on the artificial signals and the actual tremor signals, the performance of the proposed algorithm is evaluated. The experimental results indicate that the developed algorithm could quickly adapt to the unknown dominant frequency and extract the tremor components with high accuracy. Therefore, this method could be employed in the tremor suppression by FES without affecting the voluntary movement.