Robot Learning from Demonstration

A survey of robot learning from demonstration

Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227

Reinforcement learning in robotics: A survey

Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160

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) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015430 · Cited by 2,769

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 1,314

Emotion and sociable humanoid robots

Breazeal, C. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00018-1 · Cited by 1,279

Overcoming Exploration in Reinforcement Learning with Demonstrations

Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8463162 · Cited by 712

Diffusion policy: Visuomotor policy learning via action diffusion

Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., Tedrake, R., & Song, S. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649241273668 · Cited by 613

Computational approaches to motor learning by imitation

Schaal, S., Ijspeert, A., & Billard, A. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1258 · Cited by 587

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Chi, C., Feng, S., Du, Y., Xu, Z., Cousineau, E., Burchfiel, B., & Song, S. (2023) · Robotics: Science and Systems XIX · doi:10.15607/rss.2023.xix.026 · Cited by 530

Model learning for robot control: A survey

Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503

Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning

Salvato, E., Fenu, G., Medvet, E., & Pellegrino, F. A. (2021) · IEEE Access · doi:10.1109/access.2021.3126658 · Cited by 200

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Nair, A., Gupta, A., Dalal, M., & Levine, S. (2020) · arXiv · doi:10.48550/arxiv.2006.09359 · Cited by 175

Implicit Behavioral Cloning

Florence, P., Lynch, C., Zeng, A., Ramirez, O., Wahid, A., Downs, L., Wong, A., Lee, J., Mordatch, I., & Tompson, J. (2021) · arXiv · doi:10.48550/arxiv.2109.00137 · Cited by 131

Imitating Latent Policies from Observation

Edwards, A. D., Sahni, H., Schroecker, Y., & Isbell, C. L. (2018) · arXiv · doi:10.48550/arxiv.1805.07914 · Cited by 79

Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation

Fong, J., Ocampo, R., Gross, D. P., & Tavakoli, M. (2020) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-020-09888-w · Cited by 52

Observational Learning by Reinforcement Learning

Borsa, D., Piot, B., Munos, R., & Pietquin, O. (2017) · arXiv · doi:10.48550/arxiv.1706.06617 · Cited by 47

Observational Learning by Reinforcement Learning

Borsa, D., Heess, N., Piot, B., Liu, S., Hasenclever, L., Munos, R., & Pietquin, O. (2019) · International Joint Conference on Autonomous Agents and Multiagent Systems · doi:10.65109/nqzd5621 · Cited by 47

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

Liu, E. Z., Guu, K., Pasupat, P., Shi, T., & Liang, P. (2018) · arXiv · doi:10.48550/arxiv.1802.08802 · Cited by 47

XIRL: Cross-embodiment inverse reinforcement learning

Zakka, K., Zeng, A., Florence, P., Tompson, J., Bohg, J., & Dwibedi, D. (2021) · arXiv · doi:10.48550/arxiv.2106.03911 · Cited by 38

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Burns, C., Izmailov, P., Kirchner, J. H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.09390 · Cited by 34

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., Tedrake, R., & Song, S. (2023) · arXiv · doi:10.48550/arxiv.2303.04137 · Cited by 12

MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation

Lancaster, P., Hansen, N., Rajeswaran, A., & Kumar, V. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611121 · Cited by 8