The DOI matches its Crossref record; 114 of the 118 references it cites were found in a registry or an index.
A survey of robot learning from demonstration
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In the citing paper
“-Data Design: Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges [17,23,25-27, 31,33,41,44,74-76,105,114-116].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“-Model Management: Challenge topics on ML model management in the deployment phase, including model validation, decisions on model retraining, adversarial settings, and backwards compatibility of trained models.The governance issues in model management also fit within this category [27,34].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“As mentioned in 4.5, there are challenges on ML model management and governance [18,27,34,65,88].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“Developing the skill set for ML applications is the major challenge related to this KA [15,16,27,32,39,40,59].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“The main challenges in this category are collaboration to ensure a successful project and adequate communication with customers [17,18,21,27,40,59,83].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
A survey of socially interactive robots
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00372-x · Cited by 3,162
Apprenticeship learning via inverse reinforcement learning
Abbeel, P., & Ng, A. Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015430 · Cited by 2,855
Zhang, B., Li, C., & Lin, N. (2020) · De Gruyter eBooks · doi:10.1515/9783110595567-011 · Cited by 1,948
Maximum Entropy Inverse Reinforcement Learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2018) · doi:10.1184/r1/6555512 · Cited by 1,868
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Research works printed but not linked: 5
- T. Inamura, M. Inaba, H. Inoue, Acquisition of probabilistic behavior decision model based on the interactive teaching method, in: Proceedings of the Ninth International Conference on Advanced Robotics, ICAR’99, 1999
- R. Dillmann, M. Kaiser, A. Ude, Acquisition of elementary robot skills from human demonstration, International Symposium on Intelligent Robotic Systems, SIRS’95, 1995
- S. Schaal, D. Sternad, Programmable pattern generators, in: 3rd International Conference on Computational Intelligence in Neuroscience, 1998
- H. Friedrich, R. Dillmann, Robot programming based on a single demonstration and user intentions, in: 3rd European Workshop on Learning Robots at ECML’95, 1995
- M. Kaiser, H. Friedrich, R. Dillmann, Obtaining good performance from a bad teacher, in: Programming by Demonstration vs. Learning from Examples Workshop at ML’95, 1995
Cited by
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Hernández, J. D., Sobti, S., Sciola, A., Moll, M., & Kavraki, L. E. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2967280 · Cited by 28
Learning Contracting Vector Fields For Stable Imitation LearningPreprint
Sindhwani, V., Tu, S., & Khansari, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04878 · Cited by 31
Reinforcement Learning with Videos: Combining Offline Observations with InteractionPreprint
Schmeckpeper, K., Rybkin, O., Daniilidis, K., Levine, S., & Finn, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06507 · Cited by 57
Yamane, K., Saigusa, Y., Sakaino, S., & Tsuji, T. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3335768 · Cited by 31
Mobile robot sequential decision making using a deep reinforcement learning hyper-heuristic approach
Cui, T., Yang, X., Jia, F., Jin, J. S., Ye, Y., & Bai, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124959 · Cited by 29
Fang, Z., Chen, T., Tian, S., Jiang, D., Zhang, Z., & Li, G. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3550743 · Cited by 27
Rafii-Tari, H., Liu, J., Payne, C. J., Bicknell, C. D., & Yang, G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_62 · Cited by 27
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video
Goo, W., & Niekum, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793515 · Cited by 26
Unveiling Taxi Drivers' Strategies via cGAIL: Conditional Generative Adversarial Imitation Learning
Zhang, X., Li, Y., Zhou, X. S., & Luo, J. (2019) · Proceedings · doi:10.1109/icdm.2019.00194 · Cited by 27
Gorjup, G., Dwivedi, A., Elangovan, N., & Liarokapis, M. V. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967782 · Cited by 27
MADRaS : Multi Agent Driving Simulator
Santara, A., Rudra, S., Buridi, S. A., Kaushik, M., Naik, A., Kaul, B., & Ravindran, B. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12531 · Cited by 27
ILoSA: Interactive Learning of Stiffness and Attractors
Franzese, G., Mészáros, A., Peternel, L., & Kober, J. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636710 · Cited by 27
Leveraging Kernelized Synergies on Shared Subspace for Precision Grasping and Dexterous Manipulation
Katyara, S., Ficuciello, F., Caldwell, D. G., Siciliano, B., & Chen, F. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3110406 · Cited by 27
Hand-Object Interaction: From Human Demonstrations to Robot Manipulation
Carfì, A., Patten, T., Kuang, Y., Hammoud, A., Alameh, M., Maiettini, E., Weinberg, A. I., Faria, D. R., Mastrogiovanni, F., Alenyà, G., et al. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.714023 · Cited by 27
Spencer, J. C., Choudhury, S., Barnes, M., Schmittle, M., Chiang, M., Ramadge, P. J., & Srinivasa, S. (2021) · Autonomous Robots · doi:10.1007/s10514-021-10006-9 · Cited by 27
Learning from demonstration in robots: Experimental comparison of neural architectures
Suleman, M. U., & Awais, M. M. (2011) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2010.10.010 · Cited by 27
A Dynamic-Bayesian Network framework for modeling and evaluating learning from observation
Ontañón, S., Montaña, J. L., & González, A. J. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.02.049 · Cited by 27
Distance metrics and algorithms for task space path optimization
Holladay, R., & Srinivasa, S. S. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759814 · Cited by 26
Programming-by-Demonstration of reaching motions—A next-state-planner approach
Skoglund, A., Iliev, B., & Palm, R. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2009.12.003 · Cited by 27
Sena, A., Michael, B., & Howard, M. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967688 · Cited by 27
“Furthermore, the person teaching is fallible and prone to poor teaching behaviours such as not being able to gauge the appropriate number of demonstrations required for a robot to learn a task and struggling to identify gaps in the learners knowledge [1], [3].”
BackgroundObservational Learning by Reinforcement Learning
Borsa, D., Piot, B., Munos, R., & Pietquin, O. (2017) · arXiv · doi:10.48550/arxiv.1706.06617 · Cited by 47
Toward a user-guided manipulation framework for high-DOF robots with limited communication
Alunni, N., Phillips-Grafflin, C., Suay, H. B., Lofaro, D. M., Berenson, D., Chernova, S., Lindeman, R. W., & Oh, P. (2013) · IEEE International Conference on Technologies for Practical Robot Applications · doi:10.1109/tepra.2013.6556356 · Cited by 26
Self-citationTowards a Watson that sees: Language-guided action recognition for robots
Teo, C. L., Yang, Y., Daumé, H., Fermuller, C. M., & Aloimonos, Y. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224589 · Cited by 26
Information Processing in the Mirror Neuron System in Primates and Machines
Demiris, Y., Aziz‐Zadeh, L., & Bonaiuto, J. J. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9200-7 · Cited by 26
Bianchi, R. A. C., Santos, P. E., Silva, I. J., Junior, L. A. C., & de Mántaras, R. L. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0731-2 · Cited by 26
Shavit, Y., Figueroa, N., Salehian, S. S. M., & Billard, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2833497 · Cited by 26
Qiu, Z., Kennen, J. G., Giri, S., Walter, T., Kang, Y., & Zhang, Z. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.036 · Cited by 26
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning
Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/oiwv2979 · Cited by 28
Driving Skill Modeling Using Neural Networks for Performance-Based Haptic Assistance
Lee, H., Kim, H., & Choi, S. (2021) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3061409 · Cited by 26
Aslan, S. N., Özalp, R., Uçar, A., & Guzelis, C. (2021) · Cluster Computing · doi:10.1007/s10586-021-03348-7 · Cited by 27
Resonance as a Design Strategy for AI and Social Robots
Lomas, D., Lin, A., Dikker, S., Förster, D., Lupetti, M. L., Huisman, G., Habekost, J., Beardow, C., Pandey, P., Ahmad, N., et al. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.850489 · Cited by 29
MIND MELD: Personalized Meta-Learning for Robot-Centric Imitation Learning
Schrum, M., Hedlund-Botti, E., Moorman, N. M., & Gombolay, M. (2022) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889616 · Cited by 26
Learning Generalized Wireless MAC Communication Protocols via Abstraction
Miuccio, L., Riolo, S., Samarakoon, S., Panno, D., & Bennis, M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000805 · Cited by 26
COACH: Learning continuous actions from COrrective Advice Communicated by Humans
Celemin, C. E., & Ruíz-del-Solar, J. (2015) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251514 · Cited by 26
Believable Bot Navigation via Playback of Human Traces
Karpov, I. V., Schrum, J., & Miikkulainen, R. (2012) · Springer eBooks · doi:10.1007/978-3-642-32323-2_6 · Cited by 26
Transferring human navigation behaviors into a robot local planner
Ramón-Vigo, R., Pérez-Higueras, N., Caballero, F., & Merino, L. (2014) · IEEE RO-MAN · doi:10.1109/roman.2014.6926347 · Cited by 26
Learning search heuristics for finding objects in structured environments
Joho, D., Senk, M., & Burgard, W. (2011) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2011.02.012 · Cited by 26
Choi, S., Lee, K., & Oh, S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487168 · Cited by 26
Bianchi, M., Averta, G., Battaglia, E., Rosales, C., Bonilla, M., Alessandro, T., Poggiani, M., Santaera, G., Ciotti, S., Catalano, M. G., et al. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8463212 · Cited by 26
Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation
Zhu, F., Zhu, L., & Yang, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01165 · Cited by 26
Graph-Structured Visual ImitationPreprint
Sieb, M., Xian, Z., Huang, A., Kroemer, O., & Fragkiadaki, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.05518 · Cited by 27
AvE: Assistance via EmpowermentPreprint
Du, Y., Tiomkin, S., Kıcıman, E., Polani, D., Abbeel, P., & Dragan, A. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.14796 · Cited by 26
Fiore, S. M. (2012) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199928286.013.0039 · Cited by 25
Lu, Z., Wang, N., & Yang, C. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3370139 · Cited by 25
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
Drolet, M., Stepputtis, S., Kailas, S., Jain, A., Peters, J., Schaal, S., & Amor, H. B. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3445630 · Cited by 28
Point cloud video object segmentation using a persistent supervoxel world-model
Papon, J. A., Kulvičius, T., Aksoy, E. E., & Wörgötter, F. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696886 · Cited by 25
Learned Prioritization for Trading Off Accuracy and Speed
Jiang, J., Teichert, A. R., Daume, H., & Eisner, I. J. (2012) · Cited by 25
Depinet, M., MacAlpine, P., & Stone, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18615-3_47 · Cited by 25
LIA: A Virtual Assistant that Can Be Taught New Commands by Speech
Chkroun, M., & Azaria, A. (2019) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2018.1557972 · Cited by 25
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Amadio, F., Colomé, A., & Carme Torras, A. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2898330 · Cited by 25
Lampinen, S., Koivumäki, J., Zhu, W.-H., & Mattila, J. (2021) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2021.3091314 · Cited by 25
SA-Net: Robust State-Action Recognition for Learning from Observations
Soans, N., Asali, E., Hong, Y., & Doshi, P. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197393 · Cited by 25
On the development of a collaborative robotic system for industrial coating cells
Arrais, R., Costa, C. M., Ribeiro, P. F., Rocha, L. F., Silva, M. F., & Veiga, G. M. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06167-z · Cited by 26
Kugele, S., & Franklin, S. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.11.001 · Cited by 27
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Kim, B., Shimanuki, L., Kaelbling, L. P., & Lozano‐Pérez, T. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211038280 · Cited by 27
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Li, M., Canberk, A., Losey, D. P., & Sadigh, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560829 · Cited by 25
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Hannum, C., Li, R., & Wang, W. (2023) · Robotics · doi:10.3390/robotics12020030 · Cited by 25
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Gugliermo, S., Schaffernicht, E., Koniaris, C., & Pecora, F. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3268598 · Cited by 27
(2015) · International Conference on Artificial Intelligence · Cited by 25
Knowledge transfer for learning robot models via Local Procrustes Analysis
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Multi-Robot Inverse Reinforcement Learning under Occlusion with Interactions
Bogert, K., & Doshi, P. (2015) · Cited by 25
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Multi-Task Hierarchical Imitation Learning for Home Automation
Fox, R., Berenstein, R., Stoica, I. G., & Goldberg, K. Y. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843293 · Cited by 26
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Amiri, S., Shirazi, M. S., & Zhang, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5659 · Cited by 26
The Natural Language of ActionsPreprint
Tennenholtz, G., & Mannor, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.01119 · Cited by 25
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Kott, A., Théron, P., Drašar, M., Dushku, E., Blanc, B. L., Losiewicz, P., Guarino, A., Mancini, L. V., Panico, A., Pihelgas, M., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10664 · Cited by 31
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Wörgötter, F., Geib, C., Tamošiūnaitė, M., Aksoy, E. E., Piater, J., Xiong, H., Ude, A., Nemec, B., Kraft, D., Krüger, N., et al. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2427233 · Cited by 24
Motion learning in variable environments using probabilistic flow tubes
Dong, S., & Williams, B. C. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980530 · Cited by 24
Autonomous exploration of motor skills by skill babbling
Reinhart, F. (2016) · Autonomous Robots · doi:10.1007/s10514-016-9613-x · Cited by 27
Tensor Based Knowledge Transfer Across Skill Categories for Robot Control
Zhao, C., Hospedales, T. M., Stulp, F., & Sigaud, O. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/484 · Cited by 26
Wu, H., Guan, Y., & Rojas, J. (2019) · Applied Sciences · doi:10.3390/app9061072 · Cited by 24
Disjoint Multi-task Learning Between Heterogeneous Human-Centric Tasks
Kim, D.-J., Choi, J., Oh, T.-H., Yoon, Y., & Kweon, I. S. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00189 · Cited by 24
Improving Deep Reinforcement Learning in Minecraft with Action Advice
Frazier, S., & Riedl, M. O. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v15i1.5237 · Cited by 23
Wang, B., Gong, J., & Chen, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2941859 · Cited by 24
Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks
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Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning
Menner, M., Berntorp, K., Zeilinger, M. N., & Cairano, S. D. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.3000323 · Cited by 24
A conceptual framework for externally-influenced agents: an assisted reinforcement learning review
Bignold, A., Cruz, F., Taylor, M. E., Brys, T., Dazeley, R., Vamplew, P., & Foale, C. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03489-y · Cited by 24
Improving Autonomous Robotic Navigation Using Imitation Learning
César-Tondreau, B., Warnell, G., Stump, E., Kochersberger, K. B., & Waytowich, N. R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.627730 · Cited by 24
Automatic task decomposition and state abstraction from demonstration
Cobo, L. C., Isbell, C. L., & Thomaz, A. L. (2012) · doi:10.5555/2343576.2343645 · Cited by 24
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Ivaldi, S., Sigaud, O., Berret, B., & Nori, F. (2012) · Paladyn Journal of Behavioral Robotics · doi:10.2478/s13230-012-0022-3 · Cited by 27
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Michini, B., Cutler, M., & How, J. P. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630592 · Cited by 24
Imitation learning of hand gestures and its evaluation for humanoid robots
Thobbi, A., & Sheng, W. (2010) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2010.5512333 · Cited by 24
Bio-inspired approach to learning robot motion trajectories and visual control commands
Mitić, M., & Miljković, Z. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.053 · Cited by 24
Towards Industrial Robot Learning from Demonstration
Ko, W., Wu, Y. J., Tee, K. P., & Buchli, J. (2015) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/2814940.2814984 · Cited by 24
Topological trajectory clustering with relative persistent homology
Pokorny, F. T., Goldberg, K. Y., & Kragić, D. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487092 · Cited by 24
Berthet-Rayne, P., Power, M., King, H. H., & Yang, G. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487198 · Cited by 24
Fang, Z., Bartels, G., & Beetz, M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759590 · Cited by 24
Maintaining efficient collaboration with trust-seeking robots
Xu, A., & Dudek, G. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759510 · Cited by 24
Social interaction for efficient agent learning from human reward
Li, G., Whiteson, S., Knox, W. B., & Hung, H. (2017) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-017-9374-8 · Cited by 24
Robust Learning From Demonstrations With Mixed Qualities Using Leveraged Gaussian Processes
Choi, S., Lee, K., & Oh, S. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2891173 · Cited by 24
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Metadata
- Authors
- Argall, B. D., Chernova, S., Veloso, M., & Browning, B.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Argall, Brenna D.
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 57
- Issue
- 5
- Pages
- 469-483
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W1986014385
- Cited by
- 3,227
- References
- 118
- Citations by year
- 2026: 107 · 2025: 154 · 2024: 152 · 2023: 199 · 2022: 205 · 2021: 286 · 2020: 304 · 2019: 341 · 2018: 256 · 2017: 216 · 2016: 191 · 2015: 204 · 2014: 162 · 2013: 160 · 2012: 117
- FWCI
- 91.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Robotics and Autonomous Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- imitation learning, robot learning, teleoperation, matching function, dynamics model
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