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Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community.
An efficiently computable metric for comparing polygonal shapes
Arkin, E.M., Chew, L.P., Huttenlocher, D.P., Kedem, K., and Mitchell, J.S. (1991) · 1991
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., and Levine, S. (2017) · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017) · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., and Riedmiller, M. (2017) · 2017
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Scalable deep reinforcement learning for vision-based robotic manipulation
Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al. (2018) · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Peng, X.B., Andrychowicz, M., Zaremba, W., and Abbeel, P. (2018) · 2018
Earlier work this paper cites.
A framework for robot manipulation: Skill formalism, meta learning and adaptive control
Johannsmeier, L., Gerchow, M., and Haddadin, S. (2019) · 2019
Cited alongside, same era.
Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks
Lee, M.A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Fei-Fei, L., Garg, A., and Bohg, J. (2019) · 2019
Cited alongside, same era.
Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning
Perrone, V., Shen, H., Seeger, M.W., Archambeau, C., and Jenatton, R. (2019) · 2019
Cited alongside, same era.
Efficient off-policy meta-reinforcement learning via probabilistic context variables
Rakelly, K., Zhou, A., Finn, C., Levine, S., and Quillen, D. (2019) · 2019
Cited alongside, same era.
Learning hierarchical control for robust in-hand manipulation
Li, T., Srinivasan, K., Meng, M.Q.H., Yuan, W., and Bohg, J. (2020) · 2020
Cited alongside, same era.
Robot program parameter inference via differentiable shadow program inversion
Alt, B., Katic, D., Jäkel, R., Bozcuoglu, A.K., and Beetz, M. (2021) · 2021
Later among the works it cites.
Benchmarking off-the-shelf solutions to robotic assembly tasks
Lian, W., Kelch, T., Holz, D., Norton, A., and Schaal, S. (2021) · 2021
Later among the works it cites.
Learning sequences of manipulation primitives for robotic assembly
Vuong, N., Pham, H., and Pham, Q.C. (2021) · 2021
Later among the works it cites.
Learning dense rewards for contact-rich manipulation tasks
Wu, Z., Lian, W., Unhelkar, V., Tomizuka, M., and Schaal, S. (2021) · 2021
Later among the works it cites.
Residual learning from demonstration: Adapting dmps for contact-rich manipulation
Davchev, T., Luck, K.S., Burke, M., Meier, F., Schaal, S., and Ramamoorthy, S. (2022) · 2022
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Multi-level structure vs. end-to-end-learning in high-performance tactile robotic manipulation
Voigt, F., Johannsmeier, L., and Haddadin, S. (2020) · 2020
Cited alongside, same era.