14 accepted papers
，软握把因其适应性和安全性而备受推崇，但 它们固有的柔软性常常导致在重物下抓握失败 很多。大多数增强附着力的握把依赖单一附着 针对光滑或粗糙表面量身定制的策略。蜥蜴， 但在非结构化环境中，有效导航时，通过以下方式 基于 地表状况。灵感来自混合粘附策略 壁虎和变色龙，本研究展示了一种仿生的软抓握器 它集成了微楔干胶和SMA驱动的微棘。 微楔胶提供可控的附着力，保证平滑 而SMA驱动的微棘则延伸用于粗糙表面 粘附和回放以避免干扰。优化模型为 开发目的是确定最优链路维度，提升抓取能力 性能方面，力和半径。实验结果 各种表面验证了其有效
Soft grippers are highly valued for their adaptability and safety, but their inherent softness often leads to grasping failure under heavy loads. Most adhesion-enhanced grippers rely on single-adhesion strategies tailored for either smooth or rough surfaces. Lizards, however, effectively navigate in
Distributed task allocation in the UAV swarm is sensitive to excessive communication overhead and frequent transmissions. Combining reinforcement learning and task allocation demonstrates great potential in enhancing algorithm performance and optimizing communication. However, existing studies rely
Accurate dynamics modeling is essential for quadrotors to achieve precise trajectory tracking in various applications. Traditional physical knowledge-driven modeling methods face substantial limitations in unknown environments characterized by variable payloads, wind disturbances, and external pertu
Deep neural network (DNN)-based lipreading models have achieved excellent recognition accuracy but are currently facing challenges related to user privacy. To address this, we propose a novel hybrid quantum-classical neural network (HQCNN) for lipreading that balances superior performance with enhan…
Scene graph generation (SGG) is a structured approach to understanding real-world scenes with complex relations, which can enhance UAV autonomy in unfamiliar environments. However, SGG typically has numerous model parameters that require considerable computational resources. This paper proposes a re
In recent years, Deep Reinforcement Learning (DRL) has been a key approach to solving Unmanned Aerial Vehicle (UAV) swarm path planning problems. However, traditional DRL methods often face challenges in the initial learning stage and struggle to learn from variable step-size tasks. This paper intro…
We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumption…
In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only…
Fact verification constitutes a pivotal application in the effort to combat the dissemination of disinformation, a concern that has recently garnered considerable attention. However, previous studies in the field of fact verification, particularly those focused on question-answering dialogue, have e…
The emergence of non-fingered soft bioinspired grippers poses a challenge for learning-based grasping control due to the lack of a model describing grasping robustness and a dataset for training. In this letter, we propose a comprehensive pipeline encompassing grasping evaluation, dataset generation
Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. However, existing datasets and evaluation methods in this domain still exhibit notable limitations. In this work, we formulate…
Soft wearable devices for hand rehabilitation can assist activities of daily living. While existing soft robotic gloves can help with finger flexion, they are less suitable for patients who cannot extend their fingers due to poststroke increased muscular tension. In this letter, we proposed a variab
Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is still a challenge due to kinematic complexity and environmental uncertainty. In this paper, we deal with the decentralized flocking and collision avoidance problem through deep reinforcement learning (DRL). Specifically, we f…