Artigos de revistas sobre o tema "Deep Learning and Perception for Grasping and Manipulation"
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Han, Dong, Hong Nie, Jinbao Chen, Meng Chen, Zhen Deng e Jianwei Zhang. "Multi-modal haptic image recognition based on deep learning". Sensor Review 38, n.º 4 (17 de setembro de 2018): 486–93. http://dx.doi.org/10.1108/sr-08-2017-0160.
Texto completo da fonteValarezo Añazco, Edwin, Sara Guerrero, Patricio Rivera Lopez, Ji-Heon Oh, Ga-Hyeon Ryu e Tae-Seong Kim. "Deep Learning-Based Ensemble Approach for Autonomous Object Manipulation with an Anthropomorphic Soft Robot Hand". Electronics 13, n.º 2 (17 de janeiro de 2024): 379. http://dx.doi.org/10.3390/electronics13020379.
Texto completo da fonteWang, Cong, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot e Sen Wang. "Learning Mobile Manipulation through Deep Reinforcement Learning". Sensors 20, n.º 3 (10 de fevereiro de 2020): 939. http://dx.doi.org/10.3390/s20030939.
Texto completo da fonteZhao, Wenhui, Bin Xu e Xinzhong Wu. "Robot grasping system based on deep learning target detection". Journal of Physics: Conference Series 2450, n.º 1 (1 de março de 2023): 012071. http://dx.doi.org/10.1088/1742-6596/2450/1/012071.
Texto completo da fonteZhou, Hongyu, Jinhui Xiao, Hanwen Kang, Xing Wang, Wesley Au e Chao Chen. "Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference". Sensors 22, n.º 15 (22 de julho de 2022): 5483. http://dx.doi.org/10.3390/s22155483.
Texto completo da fonteZhang, Ruihua, Xujun Chen, Zhengzhong Wan, Meng Wang e Xinqing Xiao. "Deep Learning-Based Oyster Packaging System". Applied Sciences 13, n.º 24 (8 de dezembro de 2023): 13105. http://dx.doi.org/10.3390/app132413105.
Texto completo da fonteLiu, Ning, Cangui Guo, Rongzhao Liang e Deping Li. "Collaborative Viewpoint Adjusting and Grasping via Deep Reinforcement Learning in Clutter Scenes". Machines 10, n.º 12 (29 de novembro de 2022): 1135. http://dx.doi.org/10.3390/machines10121135.
Texto completo da fonteHan, Dong, Beni Mulyana, Vladimir Stankovic e Samuel Cheng. "A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation". Sensors 23, n.º 7 (5 de abril de 2023): 3762. http://dx.doi.org/10.3390/s23073762.
Texto completo da fonteMohammed, Marwan Qaid, Lee Chung Kwek, Shing Chyi Chua, Abdulaziz Salamah Aljaloud, Arafat Al-Dhaqm, Zeyad Ghaleb Al-Mekhlafi e Badiea Abdulkarem Mohammed. "Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion". Sustainability 13, n.º 24 (10 de dezembro de 2021): 13686. http://dx.doi.org/10.3390/su132413686.
Texto completo da fonteSayour, Malak H., Sharbel E. Kozhaya e Samer S. Saab. "Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects". Journal of Robotics 2022 (30 de junho de 2022): 1–14. http://dx.doi.org/10.1155/2022/2585656.
Texto completo da fonteRivera, Patricio, Edwin Valarezo Añazco e Tae-Seong Kim. "Object Manipulation with an Anthropomorphic Robotic Hand via Deep Reinforcement Learning with a Synergy Space of Natural Hand Poses". Sensors 21, n.º 16 (5 de agosto de 2021): 5301. http://dx.doi.org/10.3390/s21165301.
Texto completo da fonteZhang, Tengteng, e Hongwei Mo. "Research on Perception and Control Technology for Dexterous Robot Operation". Electronics 12, n.º 14 (13 de julho de 2023): 3065. http://dx.doi.org/10.3390/electronics12143065.
Texto completo da fonteMohammed, Marwan Qaid, Lee Chung Kwek, Shing Chyi Chua, Arafat Al-Dhaqm, Saeid Nahavandi, Taiseer Abdalla Elfadil Eisa, Muhammad Fahmi Miskon et al. "Review of Learning-Based Robotic Manipulation in Cluttered Environments". Sensors 22, n.º 20 (18 de outubro de 2022): 7938. http://dx.doi.org/10.3390/s22207938.
Texto completo da fonteLopez, Patricio Rivera, Ji-Heon Oh, Jin Gyun Jeong, Hwanseok Jung, Jin Hyuk Lee, Ismael Espinoza Jaramillo, Channabasava Chola, Won Hee Lee e Tae-Seong Kim. "Dexterous Object Manipulation with an Anthropomorphic Robot Hand via Natural Hand Pose Transformer and Deep Reinforcement Learning". Applied Sciences 13, n.º 1 (28 de dezembro de 2022): 379. http://dx.doi.org/10.3390/app13010379.
Texto completo da fonteBütepage, Judith, Silvia Cruciani, Mia Kokic, Michael Welle e Danica Kragic. "From Visual Understanding to Complex Object Manipulation". Annual Review of Control, Robotics, and Autonomous Systems 2, n.º 1 (3 de maio de 2019): 161–79. http://dx.doi.org/10.1146/annurev-control-053018-023735.
Texto completo da fonteCirillo, Andrea, Gianluca Laudante e Salvatore Pirozzi. "Tactile Sensor Data Interpretation for Estimation of Wire Features". Electronics 10, n.º 12 (18 de junho de 2021): 1458. http://dx.doi.org/10.3390/electronics10121458.
Texto completo da fonteZhou, Hongyu, Hanwen Kang, Xing Wang, Wesley Au, Michael Yu Wang e Chao Chen. "Branch Interference Sensing and Handling by Tactile Enabled Robotic Apple Harvesting". Agronomy 13, n.º 2 (9 de fevereiro de 2023): 503. http://dx.doi.org/10.3390/agronomy13020503.
Texto completo da fonteXie, Zhen, Josh Ye Seng Chen, Guo Wei Lim e Fengjun Bai. "Data-Driven Robotic Tactile Grasping for Hyper-Personalization Line Pick-and-Place". Actuators 12, n.º 5 (1 de maio de 2023): 192. http://dx.doi.org/10.3390/act12050192.
Texto completo da fonteCaldera, Shehan, Alexander Rassau e Douglas Chai. "Review of Deep Learning Methods in Robotic Grasp Detection". Multimodal Technologies and Interaction 2, n.º 3 (7 de setembro de 2018): 57. http://dx.doi.org/10.3390/mti2030057.
Texto completo da fonteZhang, Tengteng, e Hongwei Mo. "Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards". Entropy 26, n.º 5 (12 de maio de 2024): 416. http://dx.doi.org/10.3390/e26050416.
Texto completo da fonteChen, Ao, Yongchun Xie, Yong Wang e Linfeng Li. "Knowledge Graph-Based Image Recognition Transfer Learning Method for On-Orbit Service Manipulation". Space: Science & Technology 2021 (6 de agosto de 2021): 1–9. http://dx.doi.org/10.34133/2021/9807452.
Texto completo da fonteDesingh, Karthik. "Perception for General-purpose Robot Manipulation". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 15435. http://dx.doi.org/10.1609/aaai.v37i13.26802.
Texto completo da fonteZhu, Bo-Rui, Jin-Siang Shaw e Shih-Hao Lee. "Development of Annulus-Object Random Bin Picking System based on Rapid Establishment of RGB-D Images". WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 21 (28 de fevereiro de 2024): 128–38. http://dx.doi.org/10.37394/23209.2024.21.13.
Texto completo da fonteHuang, Shiyao, e Hao Wu. "Texture Recognition Based on Perception Data from a Bionic Tactile Sensor". Sensors 21, n.º 15 (2 de agosto de 2021): 5224. http://dx.doi.org/10.3390/s21155224.
Texto completo da fonteZhou, Huaidong, Wusheng Chou, Wanchen Tuo, Yongfeng Rong e Song Xu. "Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp". Sensors 20, n.º 22 (23 de novembro de 2020): 6697. http://dx.doi.org/10.3390/s20226697.
Texto completo da fonteZapata-Impata, Brayan S., Pablo Gil e Fernando Torres. "Tactile-Driven Grasp Stability and Slip Prediction". Robotics 8, n.º 4 (26 de setembro de 2019): 85. http://dx.doi.org/10.3390/robotics8040085.
Texto completo da fonteCordeiro, Artur, João Pedro Souza, Carlos M. Costa, Vítor Filipe, Luís F. Rocha e Manuel F. Silva. "Bin Picking for Ship-Building Logistics Using Perception and Grasping Systems". Robotics 12, n.º 1 (18 de janeiro de 2023): 15. http://dx.doi.org/10.3390/robotics12010015.
Texto completo da fontePastor, Francisco, Da-hui Lin-Yang, Jesús M. Gómez-de-Gabriel e Alfonso J. García-Cerezo. "Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning". Sensors 22, n.º 22 (12 de novembro de 2022): 8752. http://dx.doi.org/10.3390/s22228752.
Texto completo da fonteImtiaz, Muhammad Babar, Yuansong Qiao e Brian Lee. "Prehensile and Non-Prehensile Robotic Pick-and-Place of Objects in Clutter Using Deep Reinforcement Learning". Sensors 23, n.º 3 (29 de janeiro de 2023): 1513. http://dx.doi.org/10.3390/s23031513.
Texto completo da fonteYang, Zeshi, Kangkang Yin e Libin Liu. "Learning to use chopsticks in diverse gripping styles". ACM Transactions on Graphics 41, n.º 4 (julho de 2022): 1–17. http://dx.doi.org/10.1145/3528223.3530057.
Texto completo da fonteLi, Guozhen, Shiqiang Liu, Liangqi Wang e Rong Zhu. "Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition". Science Robotics 5, n.º 49 (16 de dezembro de 2020): eabc8134. http://dx.doi.org/10.1126/scirobotics.abc8134.
Texto completo da fonteAgarwal, Aditya, Yash Oza, Maxim Likhachev e Chad Kessens. "Fast and High-Quality, GPU-based, Deliberative, Object-Pose Estimation". Field Robotics 1, n.º 1 (19 de outubro de 2021): 34–69. http://dx.doi.org/10.55417/fr.2021002.
Texto completo da fonteSchwarz, Max, Anton Milan, Arul Selvam Periyasamy e Sven Behnke. "RGB-D object detection and semantic segmentation for autonomous manipulation in clutter". International Journal of Robotics Research 37, n.º 4-5 (20 de junho de 2017): 437–51. http://dx.doi.org/10.1177/0278364917713117.
Texto completo da fonteLiu, Kainan, Meiyun Zhang e Mohammed K. Hassan. "Intelligent image recognition system for detecting abnormal features of scenic spots based on deep learning". Journal of Intelligent & Fuzzy Systems 39, n.º 4 (21 de outubro de 2020): 5149–59. http://dx.doi.org/10.3233/jifs-189000.
Texto completo da fonteMassalim, Yerkebulan, Zhanat Kappassov e Huseyin Atakan Varol. "Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation". Sensors 20, n.º 15 (25 de julho de 2020): 4121. http://dx.doi.org/10.3390/s20154121.
Texto completo da fonteMeckel, Miriam, e Léa Steinacker. "Hybrid Reality: The Rise of Deepfakes and Diverging Truths". Morals & Machines 1, n.º 1 (2021): 12–23. http://dx.doi.org/10.5771/2747-5182-2021-1-12.
Texto completo da fonteMeckel, Miriam, e Léa Steinacker. "Hybrid Reality: The Rise of Deepfakes and Diverging Truths". Morals & Machines 1, n.º 1 (2021): 12–23. http://dx.doi.org/10.5771/2747-5174-2021-1-12.
Texto completo da fonteMeckel, Miriam, e Léa Steinacker. "Hybrid Reality: The Rise of Deepfakes and Diverging Truths". Morals & Machines 1, n.º 1 (2021): 10–21. http://dx.doi.org/10.5771/2747-5174-2021-1-10.
Texto completo da fonteMeckel, Miriam, e Léa Steinacker. "Hybrid Reality: The Rise of Deepfakes and Diverging Truths". Morals & Machines 1, n.º 1 (2021): 10–21. http://dx.doi.org/10.5771/2747-5182-2021-1-10.
Texto completo da fonteZhang, Haiming, Mingchang Wang, Yongxian Zhang e Guorui Ma. "TDA-Net: A Novel Transfer Deep Attention Network for Rapid Response to Building Damage Discovery". Remote Sensing 14, n.º 15 (1 de agosto de 2022): 3687. http://dx.doi.org/10.3390/rs14153687.
Texto completo da fonteLee, Jongseok, Ribin Balachandran, Konstantin Kondak, Andre Coelho, Marco De Stefano, Matthias Humt, Jianxiang Feng, Tamim Asfour e Rudolph Triebel. "Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities". Field Robotics 3, n.º 1 (10 de janeiro de 2023): 323–67. http://dx.doi.org/10.55417/fr.2023010.
Texto completo da fonteSeetohul, Jenna, e Mahmood Shafiee. "Snake Robots for Surgical Applications: A Review". Robotics 11, n.º 3 (5 de maio de 2022): 57. http://dx.doi.org/10.3390/robotics11030057.
Texto completo da fonteSharma, Santosh Kumar. "Failed Nerve Blocks: Prevention and Management". Journal of Anaesthesia and Critical Care Reports 4, n.º 3 (2018): 3–6. http://dx.doi.org/10.13107/jaccr.2018.v04i03.101.
Texto completo da fonteGorjup, Gal, Lucas Gerez e Minas Liarokapis. "Leveraging Human Perception in Robot Grasping and Manipulation Through Crowdsourcing and Gamification". Frontiers in Robotics and AI 8 (29 de abril de 2021). http://dx.doi.org/10.3389/frobt.2021.652760.
Texto completo da fonteZhao, Min, Guoyu Zuo, Shuangyue Yu, Daoxiong Gong, Zihao Wang e Ouattara Sie. "Position‐aware pushing and grasping synergy with deep reinforcement learning in clutter". CAAI Transactions on Intelligence Technology, 2 de agosto de 2023. http://dx.doi.org/10.1049/cit2.12264.
Texto completo da fontePark, Su-Young, Cheonghwa Lee, Suhwan Jeong, Junghyuk Lee, Dohyeon Kim, Youhyun Jang, Woojin Seol, Hyungjung Kim e Sung-Hoon Ahn. "Digital Twin and Deep Reinforcement Learning-Driven Robotic Automation System for Confined Workspaces: A Nozzle Dam Replacement Case Study in Nuclear Power Plants". International Journal of Precision Engineering and Manufacturing-Green Technology, 18 de março de 2024. http://dx.doi.org/10.1007/s40684-023-00593-6.
Texto completo da fonteZheng, Senjing, e Marco Castellani. "Primitive shape recognition from real-life scenes using the PointNet deep neural network". International Journal of Advanced Manufacturing Technology, 2 de agosto de 2022. http://dx.doi.org/10.1007/s00170-022-09791-z.
Texto completo da fonteKu, Subyeong, Byung-Hyun Song, Taejun Park, Younghoon Lee e Yong-Lae Park. "Soft modularized robotic arm for safe human–robot interaction based on visual and proprioceptive feedback". International Journal of Robotics Research, 20 de janeiro de 2024. http://dx.doi.org/10.1177/02783649241227249.
Texto completo da fonteDuan, Haonan, Peng Wang, Yayu Huang, Guangyun Xu, Wei Wei e Xiaofei Shen. "Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning". Frontiers in Neurorobotics 15 (9 de junho de 2021). http://dx.doi.org/10.3389/fnbot.2021.658280.
Texto completo da fonteWang, Lufeng, Qu Li, Wei Fu, Fei Jiang, Tianxing Song, Guangbo Pi e Shijie Sun. "Enhancing Automated Loading and Unloading of Ship Unloaders through Dynamic 3D Coordinate System with Deep Learning". INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 19, n.º 2 (1 de março de 2024). http://dx.doi.org/10.15837/ijccc.2024.2.6234.
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