1,721,580 research outputs found
Human-to-robot Handovers of Cups with Water
No full textIf you use this dataset in your work, please cite the following publication (currently under-review in IEEE iROS 2023).
Setup Description:
The experiment is presented as a collaborative task, where the human should help the robot clean the table by handing over the cups, from the rightmost cup to the leftmost, one at a time. The robot receives the cup; in case the cup contains water, it pours the content into the orange bucket, and finally, it places the empty cup in the blue drawer.
Participants stand in front of a table with four identical plastic cups placed in a row, equidistant from each other. These cups differ in content, being two empty and two filled with water almost to the brim, constituting two types of objects to be handover: empty or full. Participants faced a Kinova Gen3 robot fixed to a table with two distinct recipients at the robot side. On the left side of the robot, there is an orange bucket meant to contain water, while the blue drawer on the right stores the empty (or emptied) cups. We adopted a within-subject study design where participants are exposed, in a randomized order, to two conditions associated with the controller used by the robot to complete the task: a neutral motion (NEU) and an expressive motion (GAN). The neutral is a simple PID controller and the expressive is Generative Adversarial Network (GAN) which generates robot trajectories that are human-inspired from a previous dataset of humans handing over cups with water and without.
Our study involved 15 right-handed participants (8 females, 7 males, average of 26.6 (+-6.2) years old) who provided written informed consent. They were all naive regarding the purpose of the experiments and not directly involved in our research. The self-reported level of knowledge in robotics was: 40.0% professional or advanced, 33.3% average, and 26.7% little or none.
360 actions (15 participants x 12 handovers x 2 conditions) were recorded and performed successfully without dropping the cup or spilling the content.
Data Description:
All the data is synchronize using ROS timestamps.
- motion-tracking: Motion Capture data for head, shoulder, and wrist from OptiTrack at 120 Hz + IMU wrist data at 400 Hz. Inside each participant P## folder you will find two other folders P##_neu and P##_gan related to the two interaction conditions where the kinova motion controller changed during the cup pouring and cup placing. Inside each subfolder you will find the following motion tracking data: head.csv, shoulder.csv, wrist.csv, robot.csv (from OptiTrack markers, the robot.csv is the robot's base), imu.csv (from IMU in the wrist), pupil.csv (Pupil ROS node), key.csv (manual labels). All these files have ROS_timestamps that can be used to find the matching frames for each of the sensors. The key.csv are manually picked time flags we marked to define specific moments in the experiment (you can the meaning in the additional notes below).
- eye-tracking_#: Pupil-Labs head-mounted eye-trackers at 120Hz for pupil infra-red cameras, and 30 Hz for forward RGB camera. All 16 participants (P##) are present for both robot motion controllers (neutral NEU, and GAN).
- go_pro_#: GoPro 1080p video of the size view of the Human-to-robot handovers experiments at 60 Hz. Note that there were 16 participants in this experiment but 3 participants did not give permission to make their image public so we removed the following participants videos: P01, P15, P16.The cups are all identical and are made of plastic. Bought on the store Flying Tiger.
Key.csv labels:
B begin
1 2 3 for beginning of the blocks
T take
H handover
P pour
R release
(P and R are for full cups)
D drop (of the empty, it comes after H)
Q quit (end of block
Human-to-robot Handovers of Cups with Water
No full textIf you use this dataset in your work, please cite the following publication (currently under-review in IEEE iROS 2023).
Setup Description:
The experiment is presented as a collaborative task, where the human should help the robot clean the table by handing over the cups, from the rightmost cup to the leftmost, one at a time. The robot receives the cup; in case the cup contains water, it pours the content into the orange bucket, and finally, it places the empty cup in the blue drawer.
Participants stand in front of a table with four identical plastic cups placed in a row, equidistant from each other. These cups differ in content, being two empty and two filled with water almost to the brim, constituting two types of objects to be handover: empty or full. Participants faced a Kinova Gen3 robot fixed to a table with two distinct recipients at the robot side. On the left side of the robot, there is an orange bucket meant to contain water, while the blue drawer on the right stores the empty (or emptied) cups. We adopted a within-subject study design where participants are exposed, in a randomized order, to two conditions associated with the controller used by the robot to complete the task: a neutral motion (NEU) and an expressive motion (GAN). The neutral is a simple PID controller and the expressive is Generative Adversarial Network (GAN) which generates robot trajectories that are human-inspired from a previous dataset of humans handing over cups with water and without.
Our study involved 15 right-handed participants (8 females, 7 males, average of 26.6 (+-6.2) years old) who provided written informed consent. They were all naive regarding the purpose of the experiments and not directly involved in our research. The self-reported level of knowledge in robotics was: 40.0% professional or advanced, 33.3% average, and 26.7% little or none.
360 actions (15 participants x 12 handovers x 2 conditions) were recorded and performed successfully without dropping the cup or spilling the content.
Data Description:
All the data is synchronize using ROS timestamps.
- motion-tracking: Motion Capture data for head, shoulder, and wrist from OptiTrack at 120 Hz + IMU wrist data at 400 Hz. Inside each participant P## folder you will find two other folders P##_neu and P##_gan related to the two interaction conditions where the kinova motion controller changed during the cup pouring and cup placing. Inside each subfolder you will find the following motion tracking data: head.csv, shoulder.csv, wrist.csv, robot.csv (from OptiTrack markers, the robot.csv is the robot's base), imu.csv (from IMU in the wrist), pupil.csv (Pupil ROS node), key.csv (manual labels). All these files have ROS_timestamps that can be used to find the matching frames for each of the sensors. The key.csv are manually picked time flags we marked to define specific moments in the experiment (you can the meaning in the additional notes below).
- eye-tracking_#: Pupil-Labs head-mounted eye-trackers at 120Hz for pupil infra-red cameras, and 30 Hz for forward RGB camera. All 16 participants (P##) are present for both robot motion controllers (neutral NEU, and GAN).
- go_pro_#: GoPro 1080p video of the size view of the Human-to-robot handovers experiments at 60 Hz. Note that there were 16 participants in this experiment but 3 participants did not give permission to make their image public so we removed the following participants videos: P01, P15, P16.The cups are all identical and are made of plastic. Bought on the store Flying Tiger.
Key.csv labels:
B begin
1 2 3 for beginning of the blocks
T take
H handover
P pour
R release
(P and R are for full cups)
D drop (of the empty, it comes after H)
Q quit (end of block
BNDE : financiamentos com correção monetária parcial
No full textParticiparam da elaboração do trabalho: Marcello Averbug - COTEC, José Carlos R. Castello Branco - COTEC, Duarte Nuno Osório - APA, Tarcísio Arantes - AF, José Eduardo de Carvalho Pereira - AP, Marcelo Nardin - APA, Nelson Tavares Filho - COTEC.Este documento procura atender a dois objetivos: a) apresentar e dimensionar os programas de financiamento do BNDE sob correção monetária parcial; b) formular proposta de diretrizes quanto à prática de correção parcial sobre os financiamentos do BNDE
Designing for workstyle transitions: interaction design tools for software engineering
Get PDFSupervisor: Duarte Nuno Jardim Nune
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
