Fetching the paper…
Reading the bibliography…
Across the robotics field, quality demonstrations are an integral part of many control pipelines.
1910
Earlier work this paper cites.
R. Dillmann, T. Asfour, M. Do, R. Jäkel, A. Kasper, P. Azad, A. Ude, S. R. Schmidt-Rohr, and M. Lösch, “Advances in robot programming by demonstration,” KI-Künstliche Intelligenz , vol. 24, no. 4, pp. 295–303, 2010
2010
Earlier work this paper cites.
B. Akgun, M. Cakmak, J. W. Yoo, and A. L. Thomaz, “Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective,” in Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction , 2012, pp. 391–398
2012
Earlier work this paper cites.
B. Akgun, M. Cakmak, K. Jiang, and A. L. Thomaz, “Keyframe-based learning from demonstration,” International Journal of Social Robotics , vol. 4, no. 4, pp. 343–355, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
B. Fang, D. Guo, F. Sun, H. Liu, and Y. Wu, “A robotic hand-arm teleoperation system using human arm/hand with a novel data glove,” in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) , 2015, pp. 2483–2488
2015
Earlier work this paper cites.
T. Welschehold, C. Dornhege, and W. Burgard, “Learning manipulation actions from human demonstrations,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2016, pp. 3772–3777
2016
Earlier work this paper cites.
Z. Su, O. Kroemer, G. E. Loeb, G. S. Sukhatme, and S. Schaal, “Learning to switch between sensorimotor primitives using multimodal haptic signals,” in International Conference on Simulation of Adaptive Behavior . Springer, 2016, pp. 170–182
2016
Earlier work this paper cites.
C. Yang, X. Wang, L. Cheng, and H. Ma, “Neural-learning-based telerobot control with guaranteed performance,” IEEE transactions on cybernetics , vol. 47, no. 10, pp. 3148–3159, 2016
2016
Earlier work this paper cites.
H. Jin, Q. Chen, Z. Chen, Y. Hu, and J. Zhang, “Multi-leapmotion sensor based demonstration for robotic refine tabletop object manipulation task,” CAAI Transactions on Intelligence Technology , vol. 1, no. 1, pp. 104–113, 2016
2016
Earlier work this paper cites.
S. Lee, A. Koo, and J. Jhung, “Moskit: Motion sickness analysis platform for vr games,” in 2017 IEEE International Conference on Consumer Electronics (ICCE) , 2017, pp. 17–18
2017
Earlier work this paper cites.
B. Fang, F. Sun, H. Liu, D. Guo, W. Chen, and G. Yao, “Robotic teleoperation systems using a wearable multimodal fusion device,” International journal of advanced robotic systems , vol. 14, no. 4, p. 1729881417717057, 2017
2017
Earlier work this paper cites.
M. Edmonds, F. Gao, X. Xie, H. Liu, S. Qi, Y. Zhu, B. Rothrock, and S.-C. Zhu, “Feeling the force: Integrating force and pose for fluent discovery through imitation learning to open medicine bottles,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017, pp. 3530–3537
2017
Earlier work this paper cites.
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain, “Time-contrastive networks: Self-supervised learning from video,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 1134–1141
2018
Earlier work this paper cites.
A. Bajcsy, D. P. Losey, M. K. O’Malley, and A. D. Dragan, “Learning from physical human corrections, one feature at a time,” in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction , 2018, pp. 141–149
2018
Cited alongside, same era.
M. Ragaglia et al. , “Robot learning from demonstrations: Emulation learning in environments with moving obstacles,” Robotics and autonomous systems , vol. 101, pp. 45–56, 2018
2018
Cited alongside, same era.
T. Gašpar, B. Nemec, J. Morimoto, and A. Ude, “Skill learning and action recognition by arc-length dynamic movement primitives,” Robotics and autonomous systems , vol. 100, pp. 225–235, 2018
2018
Cited alongside, same era.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 879–893
2018
Cited alongside, same era.
E. Johns, “Coarse-to-fine imitation learning: Robot manipulation from a single demonstration,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 4613–4619
2021
Later among the works it cites.
2021
Later among the works it cites.
H. S. Karn Chauhan, “Xr (vr & ar) headsets quarterly model tracker: Q1 2020 – q2 2022,” 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
W. Jin, T. D. Murphey, D. Kulić, N. Ezer, and S. Mou, “Learning from sparse demonstrations,” IEEE Transactions on Robotics , 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel, “Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 5628–5635
2018
Cited alongside, same era.
D. Whitney, E. Rosen, D. Ullman, E. Phillips, and S. Tellex, “Ros reality: A virtual reality framework using consumer-grade hardware for ros-enabled robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 1–9
2018
Cited alongside, same era.
R. A. Gutierrez, E. S. Short, S. Niekum, and A. L. Thomaz, “Learning from corrective demonstrations,” in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) . IEEE, 2019, pp. 712–714
2019
Cited alongside, same era.
M. E. Walker, H. Hedayati, and D. Szafir, “Robot teleoperation with augmented reality virtual surrogates,” in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) , 2019, pp. 202–210
2019
Cited alongside, same era.
Y. Kawamura, M. Murooka, N. Hiraoka, H. Ito, K. Okada, and M. Inaba, “Learning of tool force adjustment skills by a life-sized humanoid using deep reinforcement learning and active teaching request,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 3795–3802
2020
Cited alongside, same era.
2020
Cited alongside, same era.
E. Irpan, A. Gohil, and N. TenBoer, 2021 Gaming Report , 2021
2021
Cited alongside, same era.
A. T. Le, M. Guo, N. v. Duijkeren, L. Rozo, R. Krug, A. G. Kupcsik, and M. Bürger, “Learning forceful manipulation skills from multi-modal human demonstrations,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 7770–7777
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Gams, T. Petrič, B. Nemec, and A. Ude, “Manipulation learning on humanoid robots,” Current Robotics Reports , pp. 1–13, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Younesy, “Unity robotics hub,” 2 2022. [Online]. Available: https://github.com/Unity-Technologies/Unity-Robotics-Hub/tree/main
2022
Later among the works it cites.
Y. Elashry, “Two-way communication between python 3 and unity (c#),” https://github.com/Siliconifier/Python-Unity-Socket-Communication , 2022
2022
Later among the works it cites.
2023
Closest in time.