This study introduces a new force-position impedance control strategy tailored for robotic systems engaged in the manipulation of elastic garments. Our approach is innovative for incorporating external force and impedance control within an incremental trajectory generation framework. This method allows for dynamic adjustment to prevent over-deformation and potential damage during garment handling, enabling the fabric to deform to desired states and follow intended trajectory trends without compromising textile integrity. By utilizing external forces in an adaptive, incremental manner, we offer a nuanced solution for expected garment manipulation. Our strategy demonstrates the feasibility of detailed manipulation tasks in industrial settings, bridging the gap between simulated environments and real-world operations. This effort underscores the importance of automation in enhancing textile handling processes, focusing on practical applications rather than theoretical advancements.

This research unveils a novel Multi-Critic Reinforcement Learning framework designed to navigate the multifaceted challenges associated with multi-phased garment handling tasks, notably marked by persistent contact and erratic deformations between textiles and solid bodies. These tasks, ubiquitous in domestic and industrial environments, encompass activities such as dressing, fabric printing, and pressing, and are complicated by the unpredictability of textile states and the intricacy of devising control strategies. Our reinforcement learning model combines multiple time-sequenced Critic networks with traditional Deep Deterministic Policy Gradient (DDPG) techniques, thereby equipping the system to adapt to the diverse effects of fabric distortions throughout various stages. The effectiveness of this approach is demonstrated through a multi-phase pre-printing operation and further validated by real-world implementations, showing significant improvements in coverage and a substantial reduction in wrinkle formation, with its versatility further confirmed by a complex vertical dressing task. We anticipate future applications of this framework in a range of complex problems, not just garment handling. The model used in this paper can be found at https://github.com/jkk5454/multiddpg.git.

Polarization provides valuable physical information, making it beneficial for various computer vision tasks. However, haze reduces both the color and polarization information of a scene. While existing single-image dehazing methods can restore color information, they are poor at recovering polarization information. Furthermore, current polarization-based dehazing approaches neglect the physical mechanisms of polarization degradation, resulting in inaccurate reconstruction of polarization information. In this paper, we propose a novel polarization dehazing algorithm, along with a polarization degradation model, to accurately recover both polarization and color information. First, we combine two key characteristics (the polarization achromatism prior and polarization attenuation prior) with the polarization degradation model to precisely reconstruct the scene's polarization. Then, we utilize the reconstructed polarization information to recover the color information of the scene. Finally, a multi-scale fusion optimization framework is introduced to further enhance the image quality. Our method shows excellent performance on both real-world indoor and outdoor polarized images, outperforming existing dehazing algorithms in both objective evaluation metrics and subjective visual assessment.

The infrared and visible cross-modal registration and fusion can generate more comprehensive representations of object and scene information. Previous frameworks primarily focus on addressing the modality disparities and the impact of preserving diverse modality information on the performance of registration and fusion tasks among different static image pairs. However, these frameworks overlook the practical deployment on real-world devices, particularly in the context of video streams. Consequently, the resulting video streams often suffer from instability in registration and fusion, characterized by fusion artifacts and inter-frame jitter. In light of these considerations, this paper proposes a unified registration and fusion scheme for video streams, termed RCVS. It utilizes a robust matcher and spatial-temporal calibration module to achieve stable registration of video sequences. Subsequently, RCVS combines a fast lightweight fusion network to provide stable fusion video streams for infrared and visible imaging. Additionally, we collect a infrared and visible video dataset HDO, which comprises high-quality infrared and visible video data captured across diverse scenes. Our RCVS exhibits superior performance in video stream registration and fusion tasks, adapting well to real-world demands. Overall, our proposed framework and HDO dataset offer the first effective and comprehensive benchmark in this field, solving stability and real-time challenges in infrared and visible video stream fusion while assessing different solution performances to foster development in this area.