PP-Tac: Paper Picking Using Tactile Feedback in Dexterous Robotic Hands

Robots are increasingly envisioned as human companions, assisting with everyday tasks that often involve manipulating deformable objects. Although recent advances in robotic hardware and embodied AI have expanded their capabilities, current systems still struggle with handling thin, flat, and deformable objects such as paper and fabric. This limitation arises from the lack of suitable perception techniques for robust state estimation under diverse object appearances, as well as the absence of planning techniques for generating appropriate grasp motions. To bridge these gaps, this paper introduces PP-Tac, a robotic system for picking up paper-like objects. PP-Tac features a multi-fingered robotic hand with high-resolution omnidirectional tactile sensors \sensorname. This hardware configuration enables real-time slip detection and online frictional force control that mitigates such slips. Furthermore, grasp motion generation is achieved through a trajectory synthesis pipeline, which first constructs a dataset of finger's pinching motions. Based on this dataset, a diffusion-based policy is trained to control the hand-arm robotic system. Experiments demonstrate that PP-Tac can effectively grasp paper-like objects of varying material, thickness, and stiffness, achieving an overall success rate of 87.5\%. To our knowledge, this work is the first attempt to grasp paper-like deformable objects using a tactile dexterous hand. Our project webpage can be found at: https://peilin-666.github.io/projects/PP-Tac/

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

As a key technique for enabling artificial intelligence, machine learning (ML) has been shown to be capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition and recommendation systems, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design, and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association, and routing. Each aspect is further categorized according to the adopted ML techniques. Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation, and so on are discussed.