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59 results for “EMBODIMENT”
Embodiment of virtual feet correlates with motor performance in a target-stepping task
<p>Dataset for the human factors experiment of "Embodiment of virtual feet correlates with motor performance in a target-stepping task".</p>
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments - Supplementary Data
<p><strong>Introduction</strong></p> <p>In this zip, we release the auxiliary data that is beneficial to execute the implementation of AVLEN described in our paper AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments by Sudipta Paul, Amit K Roy-Chowdhury, and Anoop Cherian, NeurIPS, 2022.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped data is 4.6G</li> <li>The unzipped folder contains: (i) a README.md file and (ii) ./AVLEN-data folder. The latter contains the following zip files. Please see the AVLEN code to see how to unzip these files into their respective folders. <ul> <li>ckpt.119.pth -- 61M </li> <li>connectivity.zip -- 1.4M </li> <li>pretrained_weights.zip -- 1.7G</li> <li>ResNet-152-imagenet.zip -- 2.9G</li> <li>semantic_audionav_dialog_approx.zip -- 2.7M</li> <li>soundspaces.zip -- 479K</li> <li>speaker_model_weights.zip -- 51M</li> </ul> </li> </ul> <p><strong>Other Resources</strong></p> <p>For the implementation of AVLEN that uses the data shared here, please visit <a href="https://www.merl.com/publications/TR2022-131">MERL TR2022-131</a>.</p> <p><strong>Citation</strong></p> <p>If you use AVLEN in your research, please cite our paper:</p> <pre><code>@InProceedings{paul2022avlen, title={AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments}, booktitle={Advances in Neural Information Processing Systems}, author={Paul, Sudipta and Roy-Chowdhury, Amit and Cherian, Anoop}, volume={35}, pages={6236--6249}, year={2022} }</code></pre> <p><strong>Copyright and License</strong></p> <p>The AVLEN dataset is released under CC-BY-SA-4.0 license.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
Free Lunch in Evolutionary Embodied Computation in Modular Robotics
<p>We demonstrate, based on anecdotal experimental results, that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can significantly increase the diversity of results obtained by evolutionary computation methods.</p>
Embodiment of virtual feet correlates with motor performance in a target-stepping task: A pilot study
<p>Dataset for the human factors pilot experiment of "Embodiment of virtual feet correlates with motor performance in a target-stepping task: A pilot study"</p>
Scalable Co-Optimization of Morphology and control in Embodied Machines
<p>Raw Data for: Scalable Co-Optimization of Morphology and control in Embodied Machines</p> <p>Trials from Nov 2, 2015 represent phase offset controllers for resolution 1000 (10^3), trails from Nov 9, 2015 represent phase offset controllers of resolution 125 (5^3), and trails from 2016 represent neural network controllers with resolutions 1000 (10^3). </p> <p>This data was produced by the code found at: https://github.com/ncheney/morphological-innovation-protection</p>
Data Associated with Manuscript Titled "Evolution of Primate Vocal Repertoires: Vocalization Systems as Embodied Capital for Mediating Within-group Conflict"
<p><span>This is a dataset used in analyses of the macroevolution of primate vocal repertoire size interpreted in the associated manuscript titled "Evolution of Primate Vocal Repertoires: Vocalization Systems as Embodied Capital for Mediating Within-group Conflict." The first tab of the data file contains the following information for each of 42 primates species: maximum longevity (years), endocranial volume (cubic centimeters), log endocranial volume, body mass (g), log body mass, group size, within-group conflict score, vocal repertoire size, and research effort (number of zoological records). The second tab of the data file contains two tables, one reports maximum longevity (years), endocranial volume (cubic centimeters), log endocranial volume, group size, within-group conflict score, and vocal repertoire size values (mean, median, standard deviation, range) aggregated at the suborder, infraorder, superfamily, and family level, while the other reports those statistics aggregated at the family level. The third tab of the data file contains ancestral node ID, ancestral node age (in millions of years), reconstructed ancestral within-group conflict values (mean, 95% lower confidence interval, 95% upper confidence interval), and reconstructed ancestral vocal repertoire size values (mean, 95% lower confidence interval, 95% upper confidence interval). A .pdf file provides visualizations of ancestral character reconstruction (ACR) models with ancestral node IDs for Z-scored within-group conflict (panel A) and Z-scored vocal repertoire size (panel B). </span></p>
The Embodiment of Colonial Strategy
<p>The Egyptian Empire conquered and colonized Nubia, what is today northern Sudan, on multiple occasions. The colonization strategy employed was highly variable through time, ranging from the construction of militarized fortresses (Middle Kingdom 2050-1650 BCE) to an amicable co-existence approach (New Kingdom 1550-1050 BCE). Egyptian tactics also varied spatially, depending on several factors including a colonized community’s utility to the empire and the potential for revolt. Using a large dataset (<em>n</em>=341), this paper compares osteoarthritis between seven Nubian communities to (1) evaluate whether imperial strategy impacted osteoarthritis severity, and (2) assess whether rates of osteoarthritis differed between colonized communities. </p> <p> </p> <p>Age-controlled ANCOVA analysis suggests there was significant variation in the frequency and severity of osteoarthritis throughout the empire. The Middle Kingdom C-Group, an indigenous Nubian population that lived outside the Egyptian built and occupied fortresses, displayed the highest rates of osteoarthritis for nearly all joint systems. Osteoarthritis then decreased during the post-co­lonial Second Intermediate Period (1650-1550 BCE) and again increased during the recolonization of the New Kingdom. However, there is significant variation of osteoarthritis at three New Kingdom sites, each of which experienced a differing colonization approach. This study suggests that the varying imperial strategies utilized by the Egyptian Empire may have impacted the physical activities and daily lives of Nubians and that these tactics were not equal throughout Nubia, but were tailored to commu­nities. It is therefore difficult to discuss a singular outcome of colonization; rather, these interpretations need to be nuanced with community-level archaeological context.</p>
Raw EEG Data Publication "Embodying the camera"
<p>Raw data files of 16 subjects recorded during experiment as described in article: Heimann, K., Uithol, S., Calbi, M., Umiltà, M.A., Guerra, M., Fingerhut, J., Gallese, V. (submitted to PLOSONE) <strong>Embodying the camera: an EEG study on the effect of camera movements on film spectators´ sensory-motor cortex activation". </strong></p> <p>event trigger value for start of videos = stim, indices for still, zoom and steady condition in 9.xls file (1=still, 2=zoom, 3=steady)</p> <p>event trigger value for slide announcing action execution =ceck (response = resp)</p> <p>Data of further analysis steps available at request to katrinheimann@cas.au.dk</p> <p> </p> <p> </p>
(Embodied) Artificial Intelligence: making robots curious
<p><strong>The following video describes how, active vision and embodied artificial inteligence contribute to the ROMI platform. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(MINCHIN) At Humboldt University we see how ‘embodied artificial intelligence’, ‘computer vision’ and ‘active vision’, have been used to create feedbacks between an environment and the robotic tools in motion.<br> <br> (HAFNER) Our big goal is to understand intelligence, and to understand the principles of intelligence in natural systems like humans and other animals, and to extract these principles and put them into algorithms and put and test them in robots. We think that not everything can be pre-programmed into the robot, but the robot has to learn and make experiences by the interaction of the real world. So I can show that here, so the robot is like a child randomly moving its arm around, and at the same time learning the correlation between the motor commands here and the position of the hand as the robot can see, by its own, by its own camera. That that approach is called ‘embodied artificial intelligence’ this adaptive approach is very much important for the ROMI project where we're partners in, because we can't program everything just by hand because the the conditions change you have difference, you don't know how the how the area looks like the maybe even the robot hardware changes, the motor changes and so we can use our adaptive methods and apply them to the ROMI robots so that they can really interact with the with the plants and extract information from the plants.<br> <br> (SCHILLACI) We have a system that implements a sort of artificial curiosity. The artificial curiosity drives the movement of the robot towards interesting locations. For instance one of our tasks is to support the 3d reconstruction of plants, and instead of using predefined movements of the 3d scanner around the plants, our system instead is trying to to make more intelligent movements to to discover parts of the object that perhaps are more interesting to look at.<br> <br> So we have the learning system running at the moment, so we have the artificial curiosity explorer based exploration that sends motor commands and at the same time this, so all the information that are recorded from from the robot which are images which you can see here, and the positions of the robot are sent to the model which is going to be trained. So you probably have a goal that is like; look at the tracks, like put the tripod at the centre of the image so that's probably one of the goal. And the idea is that the next step will be like to have to move around this goal so as soon as your system is well trained, so this goal could be could be changing, for instance because the plant is growing, so you need to to move these goals around to make the system adaptive. Tt's coming back again to the tripod this one.<br> <br> (HAFNER)<strong> </strong>In ROMI it's a very challenging task because we have to we have to cope with this these real systems and we have to cope with with wind and with weather and the plants growing unexpectedly and so it's very challenging, but it's also very fascinating.<br> <br> (SCHILLACI)<strong> </strong>So the next step will be having more movement capabilities, so you will be able to segment out parts of the images that you don't need. Antonio is actually working on segmenting out the interesting objects from the from the scene, and in particular also to avoid the arm to crash into plants. So you you take first a screenshot from from a camera that is located at the top of the robot and then so you segment out all the objects that you don't need, and the object also where the robot could crash so you your trajectories will be going around plans do not crash onto them.</p>
Supplementary information to "Material intensity and embodied CO2 benchmark for reinforced concrete structures in Brazil"
<p>This Excel file contains the electronic supplementary information to the manuscript " Material intensity and embodied CO<sub>2</sub> benchmark for reinforced concrete structures in Brazil", including detailed structural design data for the 53 analyzed buildings and the calculation of the structural material quantity and embodied CO2 indicators.</p>
Embodied Virtual Reality Therapy for Functional Neurological Symptom/ Conversion Disorder
ClinicalTrials.gov study NCT02764476. IPD Sharing: NO. Countries: 1. Publications: 1.
Sensor Glove and Non-Invasive Vibrotactile Feedback Insole to Improve Hand Prostheses Functions and Embodiment
ClinicalTrials.gov study NCT03924310. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cognitive Embodiment Activation by tDCS
ClinicalTrials.gov study NCT03094520. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Exercise in Metastatic Breast Cancer: EMBody
ClinicalTrials.gov study NCT05468034. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Usage and Health Effects of Embodied Conversational Agents Among Older Adults
ClinicalTrials.gov study NCT04510883. IPD Sharing: YES. Countries: 1. Publications: 2.
Comparative Effects of Dual Task Training and Embodied Learning on Dual Task Performance in Children With Down Syndrome
ClinicalTrials.gov study NCT06943144. IPD Sharing: NO. Countries: 1. Publications: 1.
Embodied Writing Versus Conventional Writing Practice for Handwriting
ClinicalTrials.gov study NCT07244120. IPD Sharing: NO. Countries: 1. Publications: 1.
Adapting Virtual Embodiment through Reinforcement Learning
<p>Dataset for the paper Adapting Virtual Embodiment through Reinforcement Learning.</p>
Self-Assembly and Synchronization: Crafting Music with Multi-Agent Embodied Oscillators - DATASET
<p>Dataset for amalysis replication</p>
embodied creativity
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