We study human perceptions of a robot that performs robot-to-human (R2H) handovers controlled to grasp, transport, and transfer 34 objects by mimicking human givers in human-human (H2H) handover data. Recognizing the importance of human-like robotic behavior for successful collaboration, R2H studies use models of human behavior or observations of H2H data to plan robot giver motion. However, R2H studies have been limited in object counts. In this work, we use the Human-Object-Human (HOH) dataset, consisting of H2H interactions performed by 20 giver-receiver pairs with 136 objects, to conduct an R2H study with 34 objects. We teleoperate a Kinova Gen3 manipulator to grip an object as grasped by an HOH human giver, and program it to automatically transport and orient the object to a participant by mimicking the HOH giver's trajectory and transfer pose. We survey participants on safety, naturalness, and preferred choice over linear trajectory and random orientation baselines. We find that transfer pose influences perceptions of naturalness, with HOH poses showing higher naturalness ratings. Participants prefer handovers with HOH end poses when asked to pick their preferred interaction.

Prior research on the fear of robots in blue-collar workplaces shows that perception is influenced by minority status, education level, job domains, age, and location. However, the perception of robots is not universally negative, as workers exhibit positive views when robots can improve workplace safety, enhance existing skills, or take over tasks that are viewed as less desirable. Currently, an understanding is lacking of the types of barriers to safety that a worker faces or how the job context, e.g. hours spent in heavy lifts, or demographics, e.g. weight or height, contribute to the perceptions of safety, need for and type of assistance from a co-worker, and perception of an assistive robot. Through a survey of 530 blue-collar workers, we find that 85% of the barriers to safety in the workplace are physical, psychological, and a combination of physical and psychological factors. Our survey shows that the time spent in performing heavy lifts is a significant predictor for barriers to safety, while the interaction between weight and time is a marginal predictor. We find that the barriers to safety significantly influence perception of job longevity. We observe that the worker’s height is a significant predictor to comfort levels in receiving assistance from co-workers, while weight and the interaction between height and weight are marginally significant. We find that the worker’s weight and hours spent in lifting are significant predictors for the type of assistance expected. Our findings show that the number of hours spent in lifting is a significant predictor on the type of assistance expected from a robot. Based on our results, future collaborative robots should be designed to show awareness of physical and physiological needs and cognizance to the worker’s physical traits, in particular height and weight, as well as the amount of time the worker has spent on lifting tasks.

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objective to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged over all PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available1, to all researchers as per data share protocol.1https://noncontactfingerprint2023.1ivdet.org/index.php