The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Learning and generalization of motor skills by learning from demonstration
Abstract
We provide a general approach for learning robotic motor skills from human demonstration. To represent an observed movement, a non-linear differential equation is learned such that it reproduces this movement. Based on this representation, we build a library of movements by labeling each recorded movement according to task and context (e.g., grasping, placing, and releasing). Our differential equation is formulated such that generalization can be achieved simply by adapting a start and a goal parameter in the equation to the desired position values of a movement. For object manipulation, we present how our framework extends to the control of gripper orientation and finger position. The feasibility of our approach is demonstrated in simulation as well as on the Sarcos dextrous robot arm. The robot learned a pick-and-place operation and a water-serving task and could generalize these tasks to novel situations.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Is imitation learning the route to humanoid robots?
Schaal, S. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01327-3 · Cited by 1,363
Movement imitation with nonlinear dynamical systems in humanoid robots
Ijspeert, A. J., Nakanishi, J., & Stefan Schaal, A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1014739 · Cited by 862
Self-citationRobot learning from demonstration
Billard, A., & Siegwart, R. (2004) · Robotics Auton. Syst. · doi:10.1016/j.robot.2004.03.001 · Cited by 833
Computational approaches to motor learning by imitation
Schaal, S., Ijspeert, A. J., & Billard, A. (2003) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2002.1258 · Cited by 593
Self-citationImitation in Animals and Artifacts
Dautenhahn, K., & Nehaniv, C. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/3676.001.0001 · Cited by 586
Learning Attractor Landscapes for Learning Motor Primitives
Ijspeert, A. J., Nakanishi, J., Stefan Schaal, A., Becker, S., Thrun, S., & Obermayer, K. (2002) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 572
Behavioral dynamics of steering, obstable avoidance, and route selection.
Fajen, B. R., & Warren, W. H. (2003) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.29.2.343 · Cited by 453
Operational Space Control: A Theoretical and Empirical Comparison
Nakanishi, J., Cory, R., Mistry, M., Peters, J., & Stefan Schaal, A. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908091463 · Cited by 381
Hoffmann, H., Pástor, P., Park, D., & Schaal, S. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152423 · Cited by 290
Self-citationClosed-loop manipulator control using quaternion feedback
Yuan, J. S. (1988) · IEEE Journal on Robotics and Automation · doi:10.1109/56.809 · Cited by 269
Flagged first, then by citations
Research works printed but not linked: 1
- demiris, 1996, Imitative Learning Mechanisms in Robots and Humans, Proc Eur Workshop on Learning Robotics
Cited by
Sorted by citations
Items 101–200 of 701
Learning Stylistic Dynamic Movement Primitives from multiple demonstrations
Matsubara, T., Hyon, S.-H., & Morimoto, J. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5651049 · Cited by 56
Learning Dexterous Manipulation Policies from Experience and ImitationPreprint
Kumar, V., Gupta, A., Todorov, E. V., & Levine, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05095 · Cited by 56
Learning coupling terms for obstacle avoidance
Rai, A., Meier, F., Ijspeert, A. J., & Stefan Schaal, A. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041410 · Cited by 55
Design and Control of Roller Grasper V2 for In-Hand Manipulation
Yuan, S., Shao, L., Yako, C. L., Gruebele, A. M., & Salisbury, J. K. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340953 · Cited by 55
The KIT Bimanual Manipulation Dataset
Krebs, F., Meixner, A., Patzer, I., & Asfour, T. (2021) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids47582.2021.9555788 · Cited by 53
Self-citationLearning deep movement primitives using convolutional neural networks
Pervez, A., Mao, Y., & Lee, D. (2017) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2017.8246874 · Cited by 54
Merging Position and Orientation Motion Primitives
Saveriano, M. (n.d.) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 54
Conditional Neural Movement Primitives
Seker, M. Y., Imre, M., Piater, J., & Uğur, E. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.071 · Cited by 53
DDCO: Discovery of Deep Continuous Options for Robot Learning from DemonstrationsPreprint
Krishnan, S., Fox, R., Stoica, I. G., & Goldberg, K. Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.05421 · Cited by 53
Parrot: Data-Driven Behavioral Priors for Reinforcement LearningPreprint
Singh, A., Liu, H., Zhou, G., Yu, A., Rhinehart, N., & Levine, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.10024 · Cited by 101
Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations
Shao, L., Migimatsu, T., Zhang, Q., Yang, K., & Bohg, J. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.082 · Cited by 54
Movement primitives via optimization
Dragan, A. D., Muelling, K., Bagnell, J. A., & Srinivasa, S. S. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139510 · Cited by 52
Sung, J., Jin, S. H., & Saxena, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-60916-4_40 · Cited by 52
“on high level task composition [32, 10] or is often limited to previously seen objects [38, 37].”
BackgroundExtracting data from human manipulation of objects towards improving autonomous robotic grasping
Faria, D. R., Martins, R., Lobo, J., & Dias, J. M. M. (2011) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2011.07.020 · Cited by 52
Robotic Skill Learning for Precision Assembly With Microscopic Vision and Force Feedback
Qin, F., Xu, D., Zhang, D., & Li, Y. (2019) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2019.2909081 · Cited by 52
Learning feedback terms for reactive planning and control
Rai, A., Sutanto, G., Stefan Schaal, A., & Meier, F. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989252 · Cited by 50
TACO: Learning Task Decomposition via Temporal Alignment for ControlPreprint
Shiarlis, K., Wulfmeier, M., Salter, S., Whiteson, S., & Posner, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01840 · Cited by 50
Sentis, L., Petersen, J., & Philippsen, R. (2013) · Autonomous Robots · doi:10.1007/s10514-013-9358-8 · Cited by 49
Latent spaces for dynamic movement primitives
Bitzer, S., & Vijayakumar, S. (2009) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2009.5379530 · Cited by 49
Efficient movement representation by embedding Dynamic Movement Primitives in deep autoencoders
Chen, N., Bayer, J., Urban, S., & van der Smagt, P. (2015) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2015.7363570 · Cited by 49
A Robot Motion Learning Method Using Broad Learning System Verified by Small-Scale Fish-Like Robot
Xu, S., Xu, T., Li, D., Yang, C., Huang, C., & Wu, X. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2023.3269773 · Cited by 49
Teaching a humanoid robot to draw ‘Shapes’
Mohan, V., Morasso, P., Zenzeri, J., Giorgio Metta, B., Chakravarthy, V. S., & Sandini, G. (2011) · Autonomous Robots · doi:10.1007/s10514-011-9229-0 · Cited by 48
Learning manipulation actions from a few demonstrations
Abdo, N., Kretzschmar, H., Spinello, L., & Stachniss, C. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630734 · Cited by 48
Online Trajectory Planning in Dynamic Environments for Surgical Task Automation
Osa, T., Sugita, N., & Mitsuishi, M. (2014) · Robotics: Science and Systems · doi:10.15607/rss.2014.x.011 · Cited by 48
Merging Position and orientation Motion Primitives
Saveriano, M., Franzel, F., & Lee, D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793786 · Cited by 48
An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion
Li, C., Fahmy, A., & Sienz, J. (2019) · Sensors · doi:10.3390/s19204586 · Cited by 48
Embodied Communication: How Robots and People Communicate Through Physical Interaction
Kalinowska, A., Pilarski, P. M., & Murphey, T. D. (2023) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-070122-102501 · Cited by 52
Mohammad, Y. F. O., & Nishida, T. (2015) · Advanced information and knowledge processing · doi:10.1007/978-3-319-25232-2_13 · Cited by 47
Learning Contact-Rich Manipulation Skills with Guided Policy SearchPreprint
Levine, S., Wagener, N., & Abbeel, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.05611 · Cited by 45
From Virtual Demonstration to Real-World Manipulation Using LSTM and MDN
Rahmatizadeh, R., Abolghasemi, P., Behal, A., & Bölöni, L. L. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12099 · Cited by 46
Deep Imitation Learning for Bimanual Robotic ManipulationPreprint
Xie, F., Chowdhury, A., Kaluza, M. C. D. P., Zhao, L., Wong, L. L. S., & Yu, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05134 · Cited by 44
Triantafyllidis, E., Acero, F., Liu, Z., & Li, Z. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00709-2 · Cited by 41
Reinhart, F., & Steil, J. J. (2009) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2009.5379558 · Cited by 42
Toward persistent autonomous intervention in a subsea panel
Palomeras, N., Carrera, A., Hurtós, N., Karras, G. C., Bechlioulis, C. P., Cashmore, M., Magazzeni, D., Long, D. P., Fox, M., Kyriakopoulos, K. J., et al. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9511-7 · Cited by 43
Extracting low-dimensional control variables for movement primitives
Rueckert, E., Mundo, J., Paraschos, A., Peters, J., & Neumann, G. M. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139390 · Cited by 41
Robot programming from demonstration, feedback and transfer
Mollard, Y., Munzer, T., Baisero, A., Toussaint, M., & Lopes, M. C. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353615 · Cited by 41
Time-Contrastive Networks: Self-Supervised Learning from VideoPreprint
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., & Levine, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.06888 · Cited by 95
Stereo vision of liquid and particle flow for robot pouring
Yamaguchi, A., & Atkeson, C. G. (2016) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2016.7803419 · Cited by 40
Skill learning for human-robot interaction using wearable device
Fang, B., Wei, X., Sun, F., Huang, H., Yu, Y., & Liu, H. (2019) · Tsinghua Science & Technology · doi:10.26599/tst.2018.9010096 · Cited by 40
Lauretti, C., Cordella, F., & Zollo, L. (2019) · International Journal of Social Robotics · doi:10.1007/s12369-019-00597-w · Cited by 40
A SURVEY OF VISION-BASED ARCHITECTURES FOR ROBOT LEARNING BY IMITATION
BANDERA, J. P., RODRÍGUEZ, J. A., Molina-Tanco, L., & Bandera, A. J. (2012) · International Journal of Humanoid Robotics · doi:10.1142/s0219843612500065 · Cited by 39
Learned graphical models for probabilistic planning provide a new class of movement primitives
Rueckert, E., Neumann, G. M., Toussaint, M., & Maass, W. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00097 · Cited by 39
Model Predictive Motion Control based on Generalized Dynamical Movement Primitives
Krug, R., & Dimitrov, D. (2014) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0100-3 · Cited by 39
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders.Preprint
Finn, C., Tan, X., Duan, Y., Darrell, T., Levine, S., & Abbeel, P. (2015) · arXiv (Cornell University) · Cited by 41
Ke, L., Wang, J., Bhattacharjee, T., Boots, B., & Srinivasa, S. S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561662 · Cited by 38
Robotic Disassembly Task Training and Skill Transfer Using Reinforcement Learning
Qu, M., Wang, Y., & Pham, D. T. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3242831 · Cited by 38
Modular inverse reinforcement learning for visuomotor behavior
Rothkopf, C. A., & Ballard, D. H. (2013) · Biological Cybernetics · doi:10.1007/s00422-013-0562-6 · Cited by 38
Learning mobile manipulation actions from human demonstrations
Welschehold, T., Dornhege, C., & Burgard, W. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206152 · Cited by 38
Composing Complex Skills by Learning Transition Policies
Lee, Y., Sun, S., Somasundaram, S., Hu, E. S., & Lim, J. J. (2018) · International Conference on Learning Representations · Cited by 38
Learning from demonstration with model-based Gaussian processPreprint
Jaquier, N., Ginsbourger, D., & Calinon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05005 · Cited by 38
Jin, Z., Si, W., Liu, A., Zhang, W., Yu, L. J., & Yang, C. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3303011 · Cited by 37
Skill learning framework for human–robot interaction and manipulation tasks
Odesanmi, G. A., Wang, Q., & Mai, J. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102444 · Cited by 40
Mohan, V., Morasso, P., Giorgio Metta, B., & Sandini, G. (2009) · Autonomous Robots · doi:10.1007/s10514-009-9127-x · Cited by 36
Transfer learning of shared latent spaces between robots with similar kinematic structure
Delhaisse, B., Esteban, D., Rozo, L. D., & Caldwell, D. G. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966379 · Cited by 36
Weitschat, R., & Aschemann, H. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2856531 · Cited by 36
Chi, M., Yao, Y., Liu, Y., & Zhong, M. (2019) · Applied Sciences · doi:10.3390/app9081535 · Cited by 36
Compliant Parametric Dynamic Movement Primitives
Uğur, E., & Girgin, H. (2019) · Robotica · doi:10.1017/s026357471900078x · Cited by 36
Simultaneously learning actions and goals from demonstration
Akgün, B., & Thomaz, A. L. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9448-x · Cited by 35
Learning position and orientation dynamics from demonstrations via contraction analysis
Ravichandar, H. C., & Dani, A. P. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9758-x · Cited by 35
Learning to plan for constrained manipulation from demonstrations
Phillips, M., Hwang, V., Chitta, S., & Likhachev, M. (2013) · Autonomous Robots · doi:10.1007/s10514-015-9440-5 · Cited by 53
Advances in Robot Programming by Demonstration
Dillmann, R., Asfour, T., Do, M., Jäkel, R., Kasper, A., Azad, P., Ude, A., Schmidt-Rohr, S. R., & Lösch, M. (2010) · Künstliche Intell. · doi:10.1007/s13218-010-0060-0 · Cited by 35
Self-citationPouring Skills with Planning and Learning Modeled from Human Demonstrations
Yamaguchi, A., Atkeson, C. G., & Ogasawara, T. (2015) · International Journal of Humanoid Robotics · doi:10.1142/s0219843615500309 · Cited by 33
Generalizing pouring actions between objects using warped parameters
Brandi, S., Kroemer, O., & Peters, J. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041426 · Cited by 34
Learning robots to grasp by demonstration
De Coninck, E., Verbelen, T., Van Molle, P., Simoens, P., & Dhoedt, B. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103474 · Cited by 34
Towards Motor Skill Learning for Robotics
Peters, J., Mülling, K., Kober, J., Nguyen-Tuong, D., & Krömer, O. (2011) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-19457-3_28 · Cited by 34
Dong, S., & Williams, B. C. (2012) · International Journal of Social Robotics · doi:10.1007/s12369-012-0155-x · Cited by 34
Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot
Lioutikov, R., Kroemer, O., Maeda, G. J., & Peters, J. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-08338-4_115 · Cited by 33
Assistance strategy for stair ascent with a robotic hip exoskeleton
Jang, J., Kim, K., Lee, J., Lim, B., & Shim, Y. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759832 · Cited by 33
Robot life-long task learning from human demonstrations: a Bayesian approach
Koenig, N. P., & Matarić, M. J. (2016) · Autonomous Robots · doi:10.1007/s10514-016-9601-1 · Cited by 33
Learning Parametric Dynamic Movement Primitives from Multiple Demonstrations
Matsubara, T., Hyon, S.-H., & Morimoto, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17537-4_43 · Cited by 33
Open-source benchmarking for learned reaching motion generation in robotics
Lemme, A., Meirovitch, Y., Khansari-Zadeh, M. S. M., Flash, T., Billard, A., & Steil, J. J. (2015) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2015-0002 · Cited by 34
Learning quadrotor maneuvers from optimal control and generalizing in real-time
Tomić, T., Maier, M., & Haddadin, S. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907087 · Cited by 33
An Approximate Inference Approach to Temporal Optimization in Optimal Control
Rawlik, K., Toussaint, M., & Vijayakumar, S. (2010) · ERA · Cited by 32
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Ota, K., Jha, D. K., Oiki, T., Miura, M., Nammoto, T., Nikovski, D. N., & Mariyama, T. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968010 · Cited by 32
Cho, N. J., Lee, S. H., Kim, J. B., & Suh, I. H. (2020) · Applied Sciences · doi:10.3390/app10082719 · Cited by 32
Learning compound multi-step controllers under unknown dynamics
Han, W., Levine, S., & Abbeel, P. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354297 · Cited by 31
Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation
Stulp, F., Fedrizzi, A., Mösenlechner, L., & Beetz, M. (2012) · Cited by 31
Iterative Learning From Novice Human Demonstrations for Output Tracking
Warrier, R. B., & Devasia, S. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2545243 · Cited by 30
Learning compliant robotic movements based on biomimetic motor adaptation
Zeng, C., Chen, X., Wang, N., & Yang, C. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103668 · Cited by 30
Imitation and mirror systems in robots through Deep Modality Blending Networks
Seker, M. Y., Ahmetoğlu, A., Nagai, Y., Asada, M., Öztop, E., & Uğur, E. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.11.004 · Cited by 33
An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks
Wang, Y., Beltran-Hernandez, C. C., Wan, W., & Harada, K. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.777363 · Cited by 30
Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives
Song, C., Liu, G., Zhang, X., Zang, X., Xu, C., & Zhao, J. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.08.007 · Cited by 29
Robot Programming by Demonstration with Crowdsourced Action Fixes
Forbes, M., Chung, M., Cakmak, M., & Rao, R. P. N. (2014) · AAAI Conference on Human Computation & Crowdsourcing · doi:10.1609/hcomp.v2i1.13164 · Cited by 41
A nonparametric Bayesian approach toward robot learning by demonstration
Chatzis, S., Korkinof, D., & Demiris, Y. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.02.005 · Cited by 28
A novel DMP formulation for global and frame independent spatial scaling in the task space
Koutras, L., & Doulgeri, Z. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223500 · Cited by 28
Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality
Haris, M., & Głowacz, A. (2022) · Sensors · doi:10.3390/s22041425 · Cited by 30
Task adaptation through exploration and action sequencing
Nemec, B., Tamošiūnaitė, M., Wörgötter, F., & Ude, A. (2009) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2009.5379568 · Cited by 28
Kim, H., Lee, H., Choi, S., Noh, Y., & Kim, H. J. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989269 · Cited by 27
Seleem, I. A., El-Hussieny, H., Assal, S. F. M., & Ishii, H. (2020) · IEEE Access · doi:10.1109/access.2020.2997636 · Cited by 27
Learning a repertoire of actions with deep neural networks
Droniou, A., Ivaldi, S., & Sigaud, O. (2014) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2014.6982986 · Cited by 27
Generalization of example movements with dynamic systems
Gams, A., & Ude, A. (2009) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2009.5379607 · Cited by 27
HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed RewardsPreprint
Krishnan, S., Garg, A., Liaw, R., Miller, L., Pokorny, F. T., & Goldberg, K. Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06508 · Cited by 27
Montgomery, W. H., Ajay, A., Finn, C., Abbeel, P., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989383 · 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
Learning real manipulation tasks from virtual demonstrations using LSTMPreprint
Rahmatizadeh, R., Abolghasemi, P., Behal, A., & Bölöni, L. L. (2016) · arXiv (Cornell University) · Cited by 26
Learning to Play Minigolf: A Dynamical System-Based Approach
Khansari-Zadeh, S. M., Kronander, K., & Billard, A. (2012) · Advanced Robotics · doi:10.1080/01691864.2012.728692 · Cited by 28
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
Saigusa, Y., Sakaino, S., & Tsuji, T. (2022) · IEEE Access · doi:10.1109/access.2022.3185651 · Cited by 27
Generalized Orientation Learning in Robot Task Space
Huang, Y., Abu‐Dakka, F. J., Silvério, J., & Caldwell, D. G. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793540 · Cited by 25
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
Metadata
- Authors
- Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S.
- Affiliations
- University of Southern California (US)
- Karlsruhe Institute of Technology (DE)
- Venue
- 2009 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2009-05
- Pages
- 763-768
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2161395589
- Cited by
- 702
- References
- 15
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 37 · 2023: 41 · 2022: 37 · 2021: 59 · 2020: 52 · 2019: 71 · 2018: 67 · 2017: 55 · 2016: 41 · 2015: 46 · 2014: 38 · 2013: 28 · 2012: 27
- FWCI
- 48.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- imitation learning, object manipulation, pick-and-place
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
