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Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.
M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, no. 6, pp. 381–395, 1981
1981
Earlier work this paper cites.
O. Khatib, “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE Journal on Robotics and Automation , vol. 3, no. 1, pp. 43–53, 1987
1987
Earlier work this paper cites.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” in Advances in neural information processing systems , 1989, pp. 305–313
1989
Earlier work this paper cites.
Z. Zhang, “Iterative point matching for registration of free-form curves and surfaces,” International journal of computer vision , vol. 13, no. 2, pp. 119–152, 1994
1994
Earlier work this paper cites.
M. Ester, H.-P. Kriegel, J. Sander, X. Xu et al. , “A density-based algorithm for discovering clusters in large spatial databases with noise.” in kdd , vol. 96, no. 34, 1996, pp. 226–231
1996
Earlier work this paper cites.
A. J. Ijspeert, J. Nakanishi, and S. Schaal, “Movement imitation with nonlinear dynamical systems in humanoid robots,” Proceedings 2002 IEEE International Conference on Robotics and Automation , vol. 2, pp. 1398–1403 vol.2, 2002
2002
Earlier work this paper cites.
A. Billard, S. Calinon, R. Dillmann, and S. Schaal, “Robot programming by demonstration,” in Springer Handbook of Robotics , 2008
2008
Earlier work this paper cites.
S. Calinon, F. D’halluin, E. L. Sauser, D. G. Caldwell, and A. Billard, “Learning and reproduction of gestures by imitation,” IEEE Robotics and Automation Magazine , vol. 17, pp. 44–54, 2010
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2012, pp. 5026–5033
2012
Earlier work this paper cites.
S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen, “Learning hand-eye coordination for robotic grasping with large-scale data collection,” in ISER , 2016, pp. 173–184
2016
Earlier work this paper cites.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in Robotics and Automation (ICRA), 2016 IEEE Int’l Conference on . IEEE, 2016
2016
Earlier work this paper cites.
K.-T. Yu, M. Bauza, N. Fazeli, and A. Rodriguez, “More than a million ways to be pushed. a high-fidelity experimental dataset of planar pushing,” in Int’l Conference on Intelligent Robots and Systems , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” in Conference on robot learning . PMLR, 2017, pp. 357–368
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
Earlier work this paper cites.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, S. Savarese, and L. Fei-Fei, “RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation,” in Conference on Robot Learning , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 3803–3810
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Kar, A. Prakash, M.-Y. Liu, E. Cameracci, J. Yuan, M. Rusiniak, D. Acuna, A. Torralba, and S. Fidler, “Meta-sim: Learning to generate synthetic datasets,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4551–4560
2019
Earlier work this paper cites.
C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet, “Learning latent plans from play,” in Conference on Robot Learning , 2019
2019
Earlier work this paper cites.
M. Danielczuk, M. Matl, S. Gupta, A. Li, A. Lee, J. Mahler, and K. Goldberg, “Segmenting unknown 3d objects from real depth images using mask r-cnn trained on synthetic data,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 7283–7290
2019
Earlier work this paper cites.
D. Brown, W. Goo, P. Nagarajan, and S. Niekum, “Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations,” in International conference on machine learning . PMLR, 2019, pp. 783–792
2019
Earlier work this paper cites.
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3019–3026, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto, “Visual imitation made easy,” arXiv e-prints , pp. arXiv–2008, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Pitis, E. Creager, and A. Garg, “Counterfactual data augmentation using locally factored dynamics,” Advances in Neural Information Processing Systems , vol. 33, pp. 3976–3990, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. Kaspar, J. D. M. Osorio, and J. Bock, “Sim2real transfer for reinforcement learning without dynamics randomization,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 4383–4388
2020
Cited alongside, same era.
N. Di Palo and E. Johns, “Learning multi-stage tasks with one demonstration via self-replay,” in Conference on Robot Learning . PMLR, 2022, pp. 1180–1189
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín, “Error-aware imitation learning from teleoperation data for mobile manipulation,” in Conference on Robot Learning . PMLR, 2022, pp. 1367–1378
2022
Later among the works it cites.
2022
Later among the works it cites.
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T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in Conference on robot learning . PMLR, 2020, pp. 1094–1100
2020
Cited alongside, same era.
J. Devaranjan, A. Kar, and S. Fidler, “Meta-sim2: Unsupervised learning of scene structure for synthetic data generation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16 . Springer, 2020, pp. 715–733
2020
Cited alongside, same era.
B. Wen, C. Mitash, S. Soorian, A. Kimmel, A. Sintov, and K. E. Bekris, “Robust, occlusion-aware pose estimation for objects grasped by adaptive hands,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6210–6217
2020
Cited alongside, same era.
A. Mandlekar, F. Ramos, B. Boots, S. Savarese, L. Fei-Fei, A. Garg, and D. Fox, “Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 4414–4420
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín, “What matters in learning from offline human demonstrations for robot manipulation,” in Conference on Robot Learning (CoRL) , 2021
2021
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) . IEEE, 2021, pp. 4613–4619
2021
Cited alongside, same era.
2022
Later among the works it cites.
S. Sinha, A. Mandlekar, and A. Garg, “S4rl: Surprisingly simple self-supervision for offline reinforcement learning in robotics,” in Conference on Robot Learning . PMLR, 2022, pp. 907–917
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Liang, B. Wen, K. Bekris, and A. Boularias, “Learning sensorimotor primitives of sequential manipulation tasks from visual demonstrations,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8591–8597
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Zhu, P. Stone, and Y. Zhu, “Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4126–4133, 2022
2022
Later among the works it cites.
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu, “Viola: Imitation learning for vision-based manipulation with object proposal priors,” 6th Annual Conference on Robot Learning , 2022
2022
Later among the works it cites.
S. Nasiriany, T. Gao, A. Mandlekar, and Y. Zhu, “Learning and retrieval from prior data for skill-based imitation learning,” in Conference on Robot Learning (CoRL) , 2022
2022
Later among the works it cites.
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, “Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 7327–7334, 2022
2022
Later among the works it cites.
A. Allshire, M. MittaI, V. Lodaya, V. Makoviychuk, D. Makoviichuk, F. Widmaier, M. Wüthrich, S. Bauer, A. Handa, and A. Garg, “Transferring dexterous manipulation from gpu simulation to a remote real-world trifinger,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 11 802–11 809
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
D. Li, H. Ling, S. W. Kim, K. Kreis, S. Fidler, and A. Torralba, “Bigdatasetgan: Synthesizing imagenet with pixel-wise annotations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 21 330–21 340
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Liu, Y. Wen, S. Peng, C. Lin, X. Long, T. Komura, and W. Wang, “Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII . Springer, 2022, pp. 298–315
2022
Later among the works it cites.
J. Sun, Z. Wang, S. Zhang, X. He, H. Zhao, G. Zhang, and X. Zhou, “Onepose: One-shot object pose estimation without cad models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6825–6834
2022
Later among the works it cites.
H. Xu, X. Zhan, H. Yin, and H. Qin, “Discriminator-weighted offline imitation learning from suboptimal demonstrations,” in International Conference on Machine Learning . PMLR, 2022, pp. 24 725–24 742
2022
Later among the works it cites.
2023
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2023
Closest in time.
2023
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2023
Closest in time.
T. Lee, J. Tremblay, V. Blukis, B. Wen, B.-U. Lee, I. Shin, S. Birchfield, I. S. Kweon, and K.-J. Yoon, “Tta-cope: Test-time adaptation for category-level object pose estimation,” in CVPR , 2023
2023
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B. Wen, J. Tremblay, V. Blukis, S. Tyree, T. Muller, A. Evans, D. Fox, J. Kautz, and S. Birchfield, “Bundlesdf: Neural 6-dof tracking and 3d reconstruction of unknown objects,” CVPR , 2023
2023
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P.-L. Guhur, S. Chen, R. G. Pinel, M. Tapaswi, I. Laptev, and C. Schmid, “Instruction-driven history-aware policies for robotic manipulations,” in Conference on Robot Learning . PMLR, 2023, pp. 175–187
2023
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2023
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A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on Robot Learning . PMLR, 2023, pp. 403–415
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