Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks

Social intelligence is an important requirement for enabling robots to collaborate with people. In particular, human path prediction is an essential capability for robots in that it prevents potential collision with a human and allows the robot to safely make larger movements. In this paper, we present a method for predicting the trajectory of a human who follows a haptic robotic guide without using sight, which is valuable for assistive robots that aid the visually impaired. We apply a deep learning method based on recurrent neural networks using multimodal data: (1) human trajectory, (2) movement of the robotic guide, (3) haptic input data measured from the physical interaction between the human and the robot, (4) human depth data. We collected actual human trajectory and multimodal response data through indoor experiments. Our model outperformed the baseline result while using only the robot data with the observed human trajectory, and it shows even better results when using additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201

Sample-Efficient Training of Robotic Guide Using Human Path Prediction Network

Training a robot that engages with people is challenging, because it is expensive to involve people in a robot training process requiring numerous data samples. This paper proposes a human path prediction network (HPPN) and an evolution strategy-based robot training method using virtual human movements generated by the HPPN, which compensates for this sample inefficiency problem. We applied the proposed method to the training of a robotic guide for visually impaired people, which was designed to collect multimodal human response data and reflect such data when selecting the robot's actions. We collected 1,507 real-world episodes for training the HPPN and then generated over 100,000 virtual episodes for training the robot policy. User test results indicate that our trained robot accurately guides blindfolded participants along a goal path. In addition, by the designed reward to pursue both guidance accuracy and human comfort during the robot policy training process, our robot leads to improved smoothness in human motion while maintaining the accuracy of the guidance. This sample-efficient training method is expected to be widely applicable to all robots and computing machinery that physically interact with humans

Nanotwin governed toughening mechanism in hierarchically structured materials

As an important class of natural biocomposite materials, mollusk shells possess remarkable mechanical strength and toughness as a consequence of their hierarchical structuring of soft organic and hard mineral constituents through biomineralization. Strombus gigas, one of the toughest mollusk shell (99 wt% CaCO3, 1 wt% organic), contains high density of nanoscale {110} growth twins in its third order lamellae, the basic building block of the material [1]. Although the existence of these nanotwins has been known for decades their roles and functions in mechanical behaviors and properties of biological materials are still unrevealed because numerous studies in recent years aimed to investigate the relationship between mechanical properties and the elegant nano- and hierarchical structures[1-2]. To evaluate the actual role of these nanotwins, we performed in situ TEM deformation experiment, large scale atomistic simulations and finite element modeling. With these analytic tools, we revealed nano scale twins in conch shell provide a basis of the several orders higher toughness comparing to twin free aragonite. In terms of qualitative experiment, we observed nanotwins can hinder crack propagation effectively comparing to twin free single crystal aragonite and leaving phase transformed area near crack tip (Fig 1 a-c) by in situ TEM deformation experiment. Through large scale MD simulation, we confirmed this phase transformation as a hitherto unknown toughening mechanism governed by nanoscale twins. For the quantitative comparison in terms of toughness, we performed specially designed in situ TEM experiments additionally for conch shell and aragonite single crystal so as to assess the contributions of these nanoscale twins on toughness of conch shell (Fig 1.d). By combining in situ TEM nanoscale mechanical test and FEM simulation, we found that nanotwins in 3rd order lamellar can increase fracture energy an order magnitude higher than twin free aragonite and this effect become amplified via structural hierarchy. The unique properties and structural features of nanotwinned aragonitic conch shell are expected to provide a guide to designing and fabricating hierarchically structured biomimetic materials with high toughness and high modulus

A seemingly unrelated regression model of the impact of COVID-19 risk perception on urban leisure place choices

Due to the prolonged COVID-19 pandemic and various restrictions, peoples’ leisure activity patterns significantly change. Thus, it is necessary to understand how people’s travel and leisure behaviors have changed during the COVID-19 pandemic. However, there is still a lack of empirical evidence on how individuals’ COVID-19 risk perception influences their leisure destination choice behavior. This empirical study aims to confirm the relationship between risk perception of COVID-19 and choice of leisure destination and to explore any differences between them related to demographic characteristics. A total of 537 valid samples were used for SUR model analysis by conducting an online survey targeting citizens of the Seoul metropolitan area, Korea. Our findings show that the risk perception of COVID-19 has a significant effect on the choice of leisure places. In particular, the risk perception of COVID-19 has a positive effect on the choice of natural places, disinfected areas, and socially distanced spaces while negatively influencing the choice of crowded leisure places. In addition, age and gender are more effective factors than other control variables in COVID-19 risk perception and leisure destination choices. Furthermore, this study also provides several implications for urban leisure place planners and service providers to respond to the changing leisure activity patterns caused by COVID-19

Gate tunable optical absorption and band structure of twisted bilayer graphene

We report the infrared transmission measurement on electrically gated twisted bilayer graphene. The optical absorption spectrum clearly manifests the dramatic changes such as the splitting of inter-linear-band absorption step, the shift of inter-van Hove singularity transition peak, and the emergence of very strong intra-valence (intra-conduction) band transition. These anomalous optical behaviors demonstrate consistently the non-rigid band structure modification created by the ion-gel gating through the layer-dependent Coulomb screening. We propose that this screening-driven band modification is an universal phenomenon that persists to other bilayer crystals in general, establishing the electrical gating as a versatile technique to engineer the band structures and to create new types of optical absorptions that can be exploited in electro-optical device application.Comment: 13 pages, 4 figure

Optical transitions of a single nodal ring in SrAs3_3: radially and axially resolved characterization

We perform polarized optical reflection measurements on a single nodal-ring semimetal $\rm{SrAs_3}$. For the radial and axial directions of the ring, the optical conductivity $\sigma_1(\omega)$ exhibits a flat absorption $\sigma^{\mathrm{flat}}$ over a certain frequency range. In addition, a prominent optical peak appears at 2$\Delta_{\mathrm{SOC}}$ = 30 meV. For comparison, we theoretically calculate $\sigma_1(\omega)$ using an effective model Hamiltonian and first-principles calculations, which successfully reproduces the data for both directions. The $\sigma^{\mathrm{flat}}$ establishes that the universal power-law of optical conductivity holds robustly in the nodal ring. Furthermore, key quantities of the nodal ring such as the band overlap energy, average ring radius, ring ellipticity, and the SOC-gap are determined from this comparative study. As temperature increases, $\sigma_1(\omega)$ shows a substantial change, suggesting that a $T$-driven evolution occurs in the nodal ring.Comment: 6 pages, 4 figures + supplemental material (18 pages, 7 figures

Assessing Environmentally Sensitive Land to Desertification Using MEDALUS Method in Mongolia

Desertification is a global phenomenon caused by various processes, including climate change, vegetation processes, and human activities. The need to combat desertification is increasing in many countries. A reasonable assessment of the vulnerability or sensitivity of land cover to desertification at national scales is crucial to formulate appropriate strategies or policies for combating it. The main purpose of this work was to quantitatively assess the sensitivity of land cover to desertification in Mongolia using the MEDALUS approach. The MEDALUS method is a widely known technique for assessing desertification in the Mediterranean area. In this study, the method was adjusted to be applied to Mongolia, while the numerical methods of the MEDALUS remained the same. The modified MEDALUS method used nine factors from 2003 and 2008 to quantify the sensitivity of land to desertification. As a result, our study resulted in the calculation and spatial distribution of the Environmental Sensitive Area Index (ESAI), produced throughout Mongolia. In 2003, the middle region of the southern Mongolia had the highest sensitivity to desertification, while sensitivity in 2008 increased in the western area. Mongolia’s area with the highest ESAI range increased approximately five times, indicating rapid desertification occurring throughout Mongolia from 2003 to 2008