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opencc-zeroJun 2024View details →

Dataset: Richtech Robotics Inc. (RR) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: First Trust Nasdaq Artificial Intelligence and Robotics ETF (ROBT) Stock Performance

Dataset for the article: Robotic Feet Modeled After Ungulates Improve Locomotion on Soft Wet Grounds

<div> <div>This repository contains data for three different experiments presented in the paper:</div> <br> <div>(1) moose_feet (40 files): The moose leg experiments are labeled as ax_y.nc,</div> <div>where 'a' indicates attached digits and 'f' indicates free digits. The</div> <div>number 'x' is either 1 (front leg) or 2 (hind leg), and the number 'y'</div> <div>is an increment from 0 to 9 representing the 10 samples of each set.</div> <br> <div>(2) synthetic_feet (120 files): The synthetic feet experiments are labeled</div> <div>as lw_a_y.nc, where 'lw' (Low Water content) can be replaced by 'mw'</div> <div>(Medium Water content) or 'vw' (Vast Water content). The 'a' can be 'o'</div> <div>(Original Go1 foot), 'r' (Rigid extended foot), 'f' (Free digits anisotropic</div> <div>foot), or 'a' (Attached digits). Similar to (1), the last number is an increment from 0 to 9.</div> <br> <div>(3) Go1 (15 files): The locomotion experiments of the quadruped robot on the</div> <div>track are labeled as condition_y.nc, where 'condition' is either 'hard_ground'</div> <div>for experiments on hard ground, 'bioinspired_feet' for the locomotion of the</div> <div>quadruped on mud using bio-inspired anisotropic feet, or 'original_feet' for</div> <div>experiments where the robot used the original Go1 feet. The 'y' is an increment from 0 to 4.</div> <br> <div>The files for moose_feet and synthetic_feet contain timestamp (s), position (m), and force (N) data.</div> <div>The files for Go1 contain timestamp (s), position (rad), velocity (rad/s), torque (Nm) data for all 12 motors, and the distance traveled by the robot (m).</div> <br> <div>All files can be read using xarray datasets (https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).</div> </div>

Programming codes for obtaining components by means of Single Point Incremental Forming using a Kuka Robot

<p>Programming codes for obtaining components by means of Single Point Incremental Forming using a Kuka Robot, adopting different strategies</p>

A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction

<p>This file contains human-robot interaction data acquired during an  experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>

Data and Code to Accompany: "On-Body Textile Hysteresis Estimation for Personalized Physical Human-Robot Interaction"

<p>Data files, Matlab code, and figures to accompany "On-Body Textile Hysteresis Estimation for Personalized Physical Human-Robot Interaction" (submitted for peer-review on 7/20/2024).</p>

Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>

Figure 9. The impossible figure (right) is not noticeable as such at first glance-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>In the lexical domain a similar effect of holistic word processing is described in (Anstis,<br> 2005b). The viewers were presented with pairs of three-letter words in quick succession and asked<br> to report if the upper halves of the successively presented words were identical. Surprisingly, even<br> when the upper halves of the words were orthographically identical, the error rate was reliably<br> higher than expected and in comparison with matching identical successive words. As the author of<br> the study Stuart Anstis points out: “students were processing the words not as separable parts, but<br> holistically as perceptual units that could not be perceptually split apart. These results show that in<br> normal circumstances, the visual system cannot, or does not, divide words into upper and lower<br> halves” (Anstis, 2005b, p. 239).The author relates the results of his study to studies of visual<br> perception of faces as evidence that the mechanism of holistic processing in the visual and the<br> lexical domains is essentially the same.</p>

Figure 8. Machine faces, perceived as more figure-like(left) and less figure-like (right)-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>In an experimental study on visual perception, addressing directly Gestalt phenomena, a new<br> Gestalt cue for figure-ground assignment was introduced (Vechera et al., 2002). The foreground<br> versus the background organization is a strong determinant for decisions on objects seen among<br> image elements. A well-known set of perceptual cues that are often called Gestalt cues are the size<br> or area, the symmetry and the convexity vs. concavity judgments. It is generally assumed that<br> figures are ‘small, symmetrical and convex’. The authors asked the question whether these cues are<br> all that are necessary for a region of the image to be judged as a figure. The main result of this study<br> is that regions in the lower portion of a stimulus array appear more figure-like than regions in the<br> upper portion of the display.</p>

Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong<br> emotional response to the distorted face to the right in figure 6.</p>

Figure 5. The distortion is barely noticeable if the faces are viewed in the reversed position-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>According to the feature-based processing theories of human faces the main elements,<br> noticed and remembered in a face, are the eyes, the nose and the mouth (Thompson, 1980; Anstis,<br> 2005a). If, however, we distort some of the elements of a face, these should influence perception,regardless of the position of the image – upright or reversed – from the observer viewpoint. Figure 5<br> presents the reversed image of the face on the left and the reversed distorted face on the right. The<br> distortion was achieved by rotating the eyes of the image in the vertical direction.<br> Figure</p>

Figure 4. Main elements of a face, according to the feature-based processing theories-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>In 1980 Peter Thompson proposed a new experimental paradigm for investigation of<br> perception, called “face thatcherization” (also named “Thomson illusion”) (Thompson, 1980).<br> Imagine that the following face, depicted in figure 4, is a photo of the then UK Prime Minister<br> Margaret Thatcher.</p>

Figure 3. Robotic faces, similar to smiley emoticons-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>The smiley Gestalt is the result from a dynamic (evolved in time) cognitive process, it is<br> immediately given in cognition, memorable, emotionally rich and socially relevant and reflects the<br> special kind of Gestalt complexity as defined by Edwin Rausch (1988). Conventional representations<br> of holistic entities like smileys or novel robotic faces come to life because they capture essential<br> Gestalt qualities of the perceived image. For example, in figure 3 the robotic faces resemble the<br> smileys in terms of the evoked internal/emotional reactions.</p>

Figure 2. Taxonomy of the educational technologies for children with ASC-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>learner (Perlin, 1997).<br> MOSOCO is an emergent technology implemented in a smartphone called “Mobile Social<br> Compass” (Escobedo et al., 2012). Six basic social skills are being encouraged by prompting the<br> user to initiate social contact. The menu displays symbols for the basic social skills – eye contact,<br> space and proximity, start an interaction, asking questions, sharing interests and finish an<br> interaction. The MOSOCO application has turned out to be an extremely useful tool as an online<br> prompt in starting, maintaining and finishing social interaction for both typical and autistic students,<br> as well as to anyone that feels need for improving their social competence.</p>

Figure 1. Necker cube depth illusion (Adapted from [http://en.wikipedia.org/wiki/Necker_cube])-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>Often the term Gestalt is used interchangeably with the term “emergent whole” (Johansson,<br> 1998). The emergence of a cognitive Gestalt structure adds dynamical and psychophysical forces,<br> which are different from the static notion of the “emergent whole”. An eminent example for the<br> dynamic nature of the emergent process is the Necker cube, which cannot be perceived as static, but<br> rotates in front of our eyes to the complete exhaustion of the eye gazing process (figure 1).</p>

Figure 7. Kanizsa square makes us see a non-existing figure – white square (Adapted from [http://en.wikipedia.org/wiki/Optical_illusion]-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

<p>A special case of Gestalt processing is the perceiving of illusions. Illusions make us see<br> things or processes that are not there – for example the Kanizsa square like the one depicted in<br> figure 7.</p>