Human-robot object handover:Recent progress and future direction

Human-robot object handover is one of the most primitive and crucial capabilities in human-robot collaboration.It is of great significance to promote robots to truly enter human production and life scenarios and serve human in numerous tasks.Remarkable progressions in the field of human-robot object handover have been made by researchers.This article reviews the recent literature on human-robot object handover.To this end,we summarize the results from multiple dimensions,from the role played by the robot(receiver or giver),to the end-effector of the robot(parallel-jaw gripper or multi-finger hand),to the robot abilities(grasp strategy or motion planning).We also implement a human-robot object handover system for anthropomorphic hand to verify human-robot object handover pipeline.This review aims to provide researchers and developers with a guideline for designing human-robot object handover methods.

A self-powered piezoelectret sensor based on foamed plastic garbage for monitoring human motions

Constructing piezoelectret based on foamed plastic garbage is an advisable strategy for obtaining self-powered flexible electromechanical sensors with good performances.Herein,a self-powered piezoelectret sensor with basic material of low density polyethylene(LDPE)foamed plastic garbage is proposed,with characteristics of easy fabrication,excellent flexibility,and high equivalent piezoelectric coefficient d33 value up to~1,100 pC/N.The output stability is verified by continuously stimulating a sensor for~180,000 cycles under low and high applied pressure,and the variations of peak outputs are less than 5.5%.Applications for measuring low-and high-pressure signals from human body are achieved.Assembled with a wristband,a sensor is demonstrated for detecting the human pulse waves.Moreover,real time human sitting information is wirelessly monitored with a smart chair based on 4 pixels sensors array.

Scalable and eco-friendly flexible loudspeakers for distributed human-machine interactions

Flexible loudspeakers that can be easily distributed in the surrounding environment are essential for creating immersive experiences in human-machine interactions,as these devices can transmit acoustic information conveniently.In this paper,we present a flexible electret loudspeaker that offers numerous benefits,such as eco-friendly,easy fabrication,flexible customization,strong durability,and excellent outputs.The output sound pressure level(SPL)and frequency response characteristic are optimized according to the simulation and experiment results.At a distance of 50 meters,a large-size loudspeaker(50×40 cm^(2))can produce an average SPL of 60 dB(normal SPL range of human voices is between 40 to 70 dB).The frequency response of our loudspeaker is high and relatively consistent up to 15 kHz,which covers the normal frequency range of human voices(<8 kHz).As demonstrated in this work,our loudspeakers can be used for scalable applications,such as being integrated with curtains or hung up like posters,offering a promising and practical solution for creating better human-machine interaction experiences.

A miniaturized mechanical antenna based on FEP/ THV unipolar electrets for extremely low frequency transmission

An electret-based mechanical antenna(EBMA),which can transmit extremely low frequency(ELF)electromagnetic signals,has the advantages of miniaturization and high transmitting efficiency,with great potential applications in air,underwater,and underground communications.To improve the charge density of the electret,which is a key factor in determining the radiation performance of an EBMA,this work proposes a fluorinated ethylene propylene/terpolymer of tetrafluoroethylene,hexafluoropropylene and vinylidene fluoride(FEP/THV)unipolar electret exhibiting negative polarity,reaching a total charge density up to-0.46 mC/m^(2) for each layer of electret.Long transmission distances can be achieved in sea water,soil,and air using a 3-layer-FEP/THV-based EBMA with a compact volume of 5×10^(-4) m^(3).As an application demonstration,binary ASili-coded ELF information of"BUAA"is successfully transmitted with a power consumption<5W.

Toward few-shot domain adaptation with perturbation-invariant representation and transferable prototypes

Domain adaptation(DA)for semantic segmentation aims to reduce the annotation burden for the dense pixellevel prediction task.It focuses on tackling the domain gap problem and manages to transfer knowledge learned from abundant source data to new target scenes.Although recent works have achieved rapid progress in this field,they still underperform fully supervised models with a large margin due to the absence of any available hints in the target domain.Considering that few-shot labels are cheap to obtain in practical applications,wc attempt to leverage them to mitigate the performance gap between DA and fully supervised methods.The key to this problem is to leverage the few-shot labels to learn robust domain-invariant predictions effectively.To this end,we first design a data perturbation strategy to enhance the robustness of the representations.Furthermore,a transferable prototype module is proposed to bridge the domain gap based on the source data and few-shot targets.By means of these proposed methods,our approach can perform on par with the fully supervised models to some extent.We conduct extensive experiments to demonstrate the effectiveness of the proposed methods and report the state-of-the-art performance on two popular DA tasks,i.e.,from GTA5 to Cityscapes and SYNTHIA to Cityscapes.

超拉伸水凝胶热电偶

可拉伸温度传感对实现人机触觉交互和温度调节至关重要,这些传感元件需要贴合特异性表面,且在拉伸条件下保持温度感知的精度.现有可拉伸温度传感器由于温度和形变引起电阻变化的相互干扰,在拉伸过程中存在固有的传感不稳定问题.本文提出了一种超可拉伸水凝胶热电偶,通过构建具有动态交联双网络的热电水凝胶,实现水凝胶热电偶的超拉伸性.通过设计的P型和N型热电水凝胶,构建热电偶单元.热电偶表现出1.93 mV K^(-1)的高塞贝克系数,即使在100%的拉伸应变下,灵敏度依然保持稳定.本文的研究结果为可拉伸温度传感器提供了一种新的策略,并有望广泛应用于智能可穿戴设备.

Decision Making in Team-Adversary Games with Combinatorial Action Space

The team-adversary game simulates many real-world scenarios in which a team of agents competes cooperatively against an adversary.However,decision-making in this type of game is a big challenge since the joint action space of the team is combinatorial and exponentially related to the number of team members.It also hampers the existing equilibrium finding algorithms from solving team-adversary games efficiently.To solve this issue caused by the combinatorial action space,we propose a novel framework based on Counterfactual Regret Minimization(CFR)framework:CFR-MIX.Firstly,we propose a new strategy representation to replace the traditional joint action strategy by using the individual action strategies of all the team members,which can significantly reduce the strategy space.To maintain the cooperation between team members,a strategy consistency relationship is proposed.Then,we transform the consistency relationship of the strategy to the regret consistency for computing the equilibrium strategy with the new strategy representation under the CFR framework.To guarantee the regret consistency relationship,a product-form decomposition method over cumulative regret values is proposed.To implement this decomposition method,our CFR-MIX framework employs a mixing layer under the CFR framework to get the final decision strategy for the team,i.e.,the Nash equilibrium strategy.Finally,we conduct experiments on games in different domains.Extensive results show that CFR-MIX significantly outperforms state-of-the-art algorithms.We hope it can help the team make decisions in large-scale team-adversary games.