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1. Examining Fitts’ and FFitts’ Law Models for Children’s Pointing Tasks on Touchscreens

   https://doi.org/10.1145/3399715.3399844  

   Woodward, J.; Cato, J.; Smith, J.; Wang, I.; Benda, B.; Anthony, L.; Ruiz, J. (January 2020, International Conference on Advanced Visual Interfaces)

   Fitts’ law has accurately modeled both children’s and adults’ pointing movements, but it is not as precise for modeling movement to small targets. To address this issue, prior work presented FFitts’ law, which is more exact than Fitts’ law for modeling adults’ finger input on touchscreens. Since children’s touch interactions are more variable than adults, it is unclear if FFitts’ law should be applied to children. We conducted a 2D target acquisition task with 54 children (ages 5-10) to examine if FFitts’ law can accurately model children’s touchscreen movement time. We found that Fitts’ law using nominal target widths is more accurate, with a R2 value of 0.93, than FFitts’ law for modeling children’s finger input on touchscreens. Our work contributes new understanding of how to accurately predict children’s finger touch performance on touchscreens. 

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2. Fit2Form: 3D Generative Model for Robot Gripper Form Design

   Ha, Huy; Agrawal, Shubham; Song, Shuran (November 2020, Proceedings of the 2020 Conference on Robot Learning)

   The 3D shape of a robot’s end-effector plays a critical role in determining it’s functionality and overall performance. Many of today’s industrial applications rely on highly customized gripper design for a given task to ensure the system’s robustness and accuracy. However, the process of manual hardware design is both costly and time-consuming, and the quality of the design is also dependent on the engineer’s experience and domain expertise, which can easily be out-dated or inaccurate. The goal of this paper is to use machine learning algorithms to automate this design process and generate task-specific gripper designs that satisfy a set of pre-defined design objectives. We model the design objectives by training a Fitness network to predict their values for a pair of gripper fingers and a grasp object. This Fitness network is then used to provide training supervision to a 3D Generative network that produces a pair of 3D finger geometries for the target grasp object. Our experiments demonstrate that the proposed 3D generative design framework generates parallel jaw gripper finger shapes that achieve more stable and robust grasps as compared to other general-purpose and task-specific gripper design algorithms. 

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3. A Novel Quotient Space Approach to Model-Based Fault Detection and Isolation: Theory and Preliminary Simulation Evaluation

   https://doi.org/10.1109/IROS51168.2021.9636026  

   Mao, Annie M.; Whitcomb, Louis L. (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   We report the development of novel fault detection and isolation (FDI) methods for model-based fault detection (MB-FD) and quotient-space fault isolation (QS-FI). This FDI approach performs MB-FD and QS-FI of single or multiple concurrent faults in plants and actuators simultaneously, without a priori knowledge of fault form, type, or dynamics. To detect faults, MB-FD characterizes deviation from nominal behavior using the plant velocity and plant and actuator parameters estimated by nullspace-based adaptive identification. To isolate (i.e. identify) faults, the QS-FI algorithm compares the estimated parameters to a nominal parameter class in progressively decreasing-dimensional quotient spaces of the parameter space. A preliminary simulation study of these proposed FDI methods applied to a three degree-of-freedom uninhabited underwater vehicle plant model shows their ability to detect as well as isolate faults for the cases of both single and multiple simultaneous faults and suggests the generalizability of the MB-FD and QS-FI approaches to any well-defined second-order plant and actuator model whose parameters enter linearly: a broad class of systems which includes aerial vehicles, marine vehicles, spacecraft, and robot arms. 

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4. Comparing the Fidelity of Contemporary Pointing with Controller Interactions on Performance of Personal Space Target Selection

   https://doi.org/10.1109/ISMAR55827.2022.00056  

   Babu, Sabarish V.; Huang, Hsiao-Chuan; Teather, Robert J.; Chuang, Jung-Hong (October 2022, 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR))

   The goal of this research is to provide much needed empirical data on how the fidelity of popular hand gesture tracked based pointing metaphors versus commodity controller based input affects the efficiency and speed-accuracy tradeoff in users’ spatial selection in personal space interactions in VR. We conduct two experiments in which participants select spherical targets arranged in a circle in personal space, or near-field within their maximum arms reach distance, in VR. Both experiments required participants to select the targets with either a VR controller or with their dominant hand’s index finger, which was tracked with one of two popular contemporary tracking methods. In the first experiment, the targets are arranged in a flat circle in accordance with the ISO 9241-9 Fitts’ law standard, and the simulation selected random combinations of 3 target amplitudes and 3 target widths. Targets were placed centered around the users’ eye level, and the arrangement was placed at either 60%, 75%, or 90% depth plane of the users’ maximum arm’s reach. In experiment 2, the targets varied in depth randomly from one depth plane to another within the same configuration of 13 targets within a trial set, which resembled button selection task in hierarchical menus in differing depth planes in the near-field. The study was conducted using the HTC Vive head-mounted display, and used either a VR controller (HTC Vive), low-fidelity virtual pointing (Leap Motion), or a high-fidelity virtual pointing (tracked VR glove) conditions. Our results revealed that low-fidelity pointing performed worse than both high-fidelity pointing and the VR controller. Overall, target selection performance was found to be worse in depth planes closer to the maximum arms reach, as compared to middle and nearer distances. 

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5. Measurement of the Higgs boson width and evidence of its off-shell contributions to ZZ production

   https://doi.org/10.1038/s41567-022-01682-0  

   Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Del Valle, A. Escalante; Frühwirth, R.; Jeitler, M.; et al (November 2022, Nature Physics)

   Abstract Since the discovery of the Higgs boson in 2012, detailed studies of its properties have been ongoing. Besides its mass, its width—related to its lifetime—is an important parameter. One way to determine this quantity is to measure its off-shell production, where the Higgs boson mass is far away from its nominal value, and relating it to its on-shell production, where the mass is close to the nominal value. Here we report evidence for such off-shell contributions to the production cross-section of two Z bosons with data from the CMS experiment at the CERN Large Hadron Collider. We constrain the total rate of the off-shell Higgs boson contribution beyond the Z boson pair production threshold, relative to its standard model expectation, to the interval [0.0061, 2.0] at the 95% confidence level. The scenario with no off-shell contribution is excluded at a p -value of 0.0003 (3.6 standard deviations). We measure the width of the Higgs boson as $${{{\varGamma }}}_{{{{{{\rm{H}}}}}}}={3.2}_{-1.7}^{+2.4}\,{{{{{\rm{MeV}}}}}}$$ Γ H = 3.2 − 1.7 + 2.4 MeV , in agreement with the standard model expectation of 4.1 MeV. In addition, we set constraints on anomalous Higgs boson couplings to W and Z boson pairs. 

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