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14 results for “quadruped”
Figure 6. Diagrammatic quadruped seen from above. Points A-D in Support polygons and symmetrical gaits in mammals
Figure 6. Diagrammatic quadruped seen from above. Points A-D represent the animal's four feet; point G represents the vertical drawn through its centre of gravity. The animal will be more nearly balanced when standing on two diagonally opposite feet (A, C) than when standing on two ipsilateral feet (A, D), as long as G is closer to line AC than to line AD – which will be the case in most situations.
Limb work and joint work minimisation reveal an energetic benefit to the elbows-back, knees-forward limb design in parasagittal quadrupeds. Supplementary material including data, simulation code and simulation results
<p>Quadrupedal animal locomotion is energetically costly. We explore two forms of mechanical work that may be relevant in imposing these physiological demands. Limb work, due to the forces and velocities between the stance foot and the centre of mass, could theoretically be zero given vertical limb forces and horizontal centre of mass path. To prevent pitching, skewed vertical force profiles would then be required, with forelimb forces high in late stance and hindlimb forces high in early stance. By contrast, joint work – the positive mechanical work performed by the limb joints – would be reduced with forces directed through the hip or shoulder joints. Measured quadruped kinetics show features consistent with compromised reduction of both forms of work, suggesting some degree of, but not perfect, inter-joint energy transfer. The elbows-back, knees-forward design reduces the joint work demand of a low limb-work, skewed, vertical force profile. This geometry allows periods of high force to be supported when the distal segment is near vertical, imposing low moments about the elbow or knee, while the shoulder or hip avoids high joint power despite high moments because the proximal segment barely rotates – translation over this period is due to rotation of the distal segment.</p>
small terracotta quadruped, Camarina, Sicily
Very small terracotta statue of a quadruped. From the site of Camarina, Ragusa, Sicily, Italy. Classical Age. 5th-4th centuries BC. In the collection of the Museo Archeologico Ibleo di Ragusa. Processed in Reality Capture from 404 images. GDH ID and Catalog No. 3781 Citation: We want to publicly acknowledge the collaboration of the ""Soprintendenza per i Beni Culturali ed Ambientali di Ragusa"" and the ""Parco Archeologico di Kamarina e Cava d'Ispica"". Our most sincere thanks to General CC, retired, Renato Scuzzarello, the archaeologist Dr. Saverio Scerra, the architect Giorgio Battaglia, the restorer Stefania Patti Occhipinti and the archaeologists Veronica Falcone and Alessandra D'Izzia, the custodians of Parco Archeologico Marco Giardina, Giovanna Brugaletta e Rosario Licitra" Source: Objaverse 1.0 / Sketchfab
Alibates Petroglyph - Quadruped - Enhanced
This one is hard to see but is a petroglyph of a four legged animal, probably a bison, from Alibates National Monument. You can also see a foot petroglyph and several cupules. [The unenhanced version is here.](https://skfb.ly/6XSP6) Source: Objaverse 1.0 / Sketchfab
Limb work and joint work minimisation reveal an energetic benefit to the elbows-back, knees-forward limb design in parasagittal quadrupeds. Supplementary material including data, simulation code and simulation results
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Terracotta figurine quadruped. Camarina, Sicily
"Terracotta figurine of a quadruped. From the site of Camarina, Ragusa, Sicily, Italy. Classical Age. 5th-4th centuries BC. In the collection of the Museo Archeologico Ibleo di Ragusa. Processed in Reality Capture from 404 images. GDH ID and Catalog No. 3782 Citation: We want to publicly acknowledge the collaboration of the ""Soprintendenza per i Beni Culturali ed Ambientali di Ragusa"" and the ""Parco Archeologico di Kamarina e Cava d'Ispica"". Our most sincere thanks to General CC, retired, Renato Scuzzarello, the archaeologist Dr. Saverio Scerra, the architect Giorgio Battaglia, the restorer Stefania Patti Occhipinti and the archaeologists Veronica Falcone and Alessandra D'Izzia, the custodians of Parco Archeologico Marco Giardina, Giovanna Brugaletta e Rosario Licitra" Source: Objaverse 1.0 / Sketchfab
Development of a Pneumatically Actuated Quadruped Robot Using Soft-Rigid Hybrid Variable-Stiffness Rotary Joints
<p>This is a supplementary video for the paper "Development of a Pneumatically Actuated Quadruped Robot Using Soft-Rigid Hybrid Variable-Stiffness Rotary Joints" submitted to Robotics.</p>
AlienGo Quadruped Robot Bags
<p>"AlienGo Quadruped Robot Bags" is a public dataset containing sensor data recorded in the AlienGo quadruped robot.<br> Overall, most of the bags include the following topics:</p> <ul> <li><strong>/livox/imu</strong> - Livox Mid-70 LiDAR data in the Livox customized format</li> <li><strong>/Aliengo/imu</strong> - inertial data regarding the robot's IMU</li> <li><strong>/tracking_camera/imu</strong> - inertial data coming from the T265 tracking camera</li> <li><strong>/tracking_camera/fisheye2/image_raw</strong> - T265 camera raw images (not included in all bags)</li> <li><strong>/tracking_camera/odom/sample</strong> - T265 camera odometry (not included in all bags)</li> </ul> <p>Despite that, there are two bags containing data from the Ouster OS1-64 LiDAR (ouster_*.bag). One indoors, and the other outdoors.</p>
Quadruped Robot IRON DOG mini
<p>12 DoF Quadruped Robot IRON DOG mini</p>
SuperAnimal-Quadruped-80K
<h1><strong>Introduction</strong></h1> <p>This dataset supports Ye et al. 2024 Nature Communications. Please cite this dataset and paper if you use this resource. Please also see Ye et al. 2024 for the full DataSheet that accompanies this download, including the meta data for how to use this data is you want to compare model results on benchmark tasks. Below is just a summary. Also see the dataset licensing below.</p> <h2>Training Data</h2> <p>It consists of being trained together on the following datasets:</p> <ul> <li><strong>AwA-Pose</strong> Quadruped dataset, see full details at (1).</li> <li><strong>AnimalPose</strong> See full details at (2).</li> <li><strong>AcinoSet</strong> See full details at (3).</li> <li><strong>Horse-30</strong> Horse-30 dataset, benchmark task is called Horse-10; See full details at (4).</li> <li><strong>StanfordDogs</strong> See full details at (5, 6).</li> <li><strong>AP-10K</strong> See full details at (7).</li> <li><strong>iRodent</strong> We utilized the iNaturalist API functions for scraping observations with the taxon ID of Suborder Myomorpha (8). The functions allowed us to filter the large amount of observations down to the ones with photos under the CC BY-NC creative license. The most common types of rodents from the collected observations are Muskrat (Ondatra zibethicus), Brown Rat (Rattus norvegicus), House Mouse (Mus musculus), Black Rat (Rattus rattus), Hispid Cotton Rat (Sigmodon hispidus), Meadow Vole (Microtus pennsylvanicus), Bank Vole (Clethrionomys glareolus), Deer Mouse (Peromyscus maniculatus), White-footed Mouse (Peromyscus leucopus), Striped Field Mouse (Apodemus agrarius). We then generated segmentation masks over target animals in the data by processing the media through an algorithm we designed that uses a Mask Region Based Convolutional Neural Networks(Mask R-CNN) (9) model with a ResNet-50-FPN backbone (10), pretrained on the COCO datasets (11). The processed 443 images were then manually labeled with both pose annotations and segmentation masks. iRodent data is banked at <a href="../record/8250392" rel="nofollow">https://zenodo.org/record/8250392</a>. </li> <li><strong>APT-36K</strong> See full details at (12).</li> </ul> <p><a title="SuperAnimal-Quadruped-80K" href="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1690988780004-AG00N6OU1R21MZ0AU9RE/modelcard-SAQ.png?format=1500w" target="_blank" rel="noopener">Here is an image with a keypoint guide.</a></p> <h2>Ethical Considerations</h2> <p>• No experimental data was collected for this model; all datasets used are cited above.</p> <h2>Caveats and Recommendations</h2> <p>• Please note that each dataest was labeled by separate labs & separate individuals, therefore while we map names to a unified pose vocabulary, there will be annotator bias in keypoint placement (See Ye et al. 2024 for our Supplementary Note on annotator bias). You will also note the dataset is highly diverse across species, but collectively has more representation of domesticated animals like dogs, cats, horses, and cattle. We recommend if performance of a model trained on this data is not as good as you need it to be, first try video adaptation (see Ye et al. 2024), or fine-tune the weights with your own labeling.</p> <h2><strong>License</strong></h2> <p>Modified MIT.</p> <p>Copyright 2023-present by Mackenzie Mathis, Shaokai Ye, and contributors. </p> <p>Permission is hereby granted to you (hereafter "LICENSEE") a fully-paid, non-exclusive,<br>and non-transferable license for academic, non-commercial purposes only (hereafter “LICENSE”)<br>to use the "DATASET" subject to the following conditions:</p> <p>The above copyright notice and this permission notice shall be included in all copies or substantial<br>portions of the Software:</p> <p>This data or resulting software may not be used to harm any animal deliberately.</p> <p>LICENSEE acknowledges that the DATASET is a research tool. <br>THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING <br>BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.</p> <p>If this license is not appropriate for your application, please contact Prof. Mackenzie W. Mathis <br>(mackenzie@post.harvard.edu) for a commercial use license.</p> <p>Please cite Ye et al if you use this DATASET in your work.</p> <p> </p> <h2>References</h2> <ol> <li>Prianka Banik, Lin Li, and Xishuang Dong. A novel dataset for keypoint detection of quadruped animals from images. ArXiv, abs/2108.13958, 2021</li> <li>Jinkun Cao, Hongyang Tang, Haoshu Fang, Xiaoyong Shen, Cewu Lu, and Yu-Wing Tai. Cross-domain adaptation for animal pose estimation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9497–9506, 2019.</li> <li>Daniel Joska, Liam Clark, Naoya Muramatsu, Ricardo Jericevich, Fred Nicolls, Alexander Mathis, Mackenzie W. Mathis, and Amir Patel. Acinoset: A 3d pose estimation dataset and baseline models for cheetahs in the wild. 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13901–13908, 2021.</li> <li>Alexander Mathis, Thomas Biasi, Steffen Schneider, Mert Yuksekgonul, Byron Rogers, Matthias Bethge, and Mackenzie W Mathis. Pretraining boosts out-of-domain robustness for pose estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1859–1868, 2021.</li> <li>Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, June 2011.</li> <li>Benjamin Biggs, Thomas Roddick, Andrew Fitzgibbon, and Roberto Cipolla. Creatures great and smal: Recovering the shape and motion of animals from video. In Asian Conference on Computer Vision, pages 3–19. Springer, 2018.</li> <li>Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, and Dacheng Tao. Ap-10k: A benchmark for animal pose estimation in the wild. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.</li> <li>iNaturalist. OGBIF Occurrence Download. <a href="https://doi.org/10.15468/dl.p7nbxt" rel="nofollow">https://doi.org/10.15468/dl.p7nbxt</a>. iNaturalist, July 2020</li> <li>Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969, 2017.</li> <li>Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection, 2016.</li> <li>Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll’ar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014</li> <li>Yuxiang Yang, Junjie Yang, Yufei Xu, Jing Zhang, Long Lan, and Dacheng Tao. Apt-36k: A large-scale benchmark for animal pose estimation and tracking. Advances in Neural Information Processing Systems, 35:17301–17313, 2022</li> </ol> <p> </p> <p><strong>Versioning Note: </strong></p> <p>- V2 includes fixes to Stanford Dog data; it affected less than 1% of the data.</p>
Supplementary Video for Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning
<p>This file contains video of the real world experiments presented in the paper "Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning". </p> <p>It also provides a high-level overview of the motivation and the theory developed in the paper.<br> </p>
Terracotta figurine of a quadruped, Camarina
"Terracotta figurine of a quadruped. From the site of Camarina, Ragusa, Sicily, Italy. Classical Age. 5th-4th centuries BC. In the collection of the Museo Archeologico Ibleo di Ragusa. Processed in Reality Capture from 387 images. GDH ID and Catalog No. 3784 Citation: We want to publicly acknowledge the collaboration of the ""Soprintendenza per i Beni Culturali ed Ambientali di Ragusa"" and the ""Parco Archeologico di Kamarina e Cava d'Ispica"". Our most sincere thanks to General CC, retired, Renato Scuzzarello, the archaeologist Dr. Saverio Scerra, the architect Giorgio Battaglia, the restorer Stefania Patti Occhipinti and the archaeologists Veronica Falcone and Alessandra D'Izzia, the custodians of Parco Archeologico Marco Giardina, Giovanna Brugaletta e Rosario Licitra" Source: Objaverse 1.0 / Sketchfab
Alibates Petroglyph - Quadruped
A four legged animal, probably a bison, petroglyph from Alibates National Moument.  Source: Objaverse 1.0 / Sketchfab
Heavy-Duty Electrically Actuated Quadruped Robot Exp. data
<p>数据解释格式可以参考 \url{https://github.com/mit-biomimetics/Cheetah-Software}</p>
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