Fetching the paper…
Reading the bibliography…
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” in NeurIPS , 1988
1988
Earlier work this paper cites.
M. McCloskey and N. J. Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” in Psychology of Learning and Motivation , 1989, vol. 24, pp. 109–165
1989
Earlier work this paper cites.
J. Kober and J. R. Peters, “Policy search for motor primitives in robotics,” in NeurIPS , 2008
2008
Earlier work this paper cites.
S. Ross and D. Bagnell, “Efficient reductions for imitation learning,” in AISTATS , 2010, pp. 661–668
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in IROS , 2012, pp. 5026–5033
2012
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Saveriano, Y. Yin, P. Falco, and D. Lee, “Data-efficient control policy search using residual dynamics learning,” in IROS , 2017, pp. 4709–4715
2017
Earlier work this paper cites.
M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller, “Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards,” 2017
2017
Earlier work this paper cites.
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu, “Reinforcement learning with unsupervised auxiliary tasks,” ICLR , 2017
2017
Earlier work this paper cites.
M. G. Bellemare, W. Dabney, and R. Munos, “A distributional perspective on reinforcement learning,” in ICML , 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Ajay, J. Wu, N. Fazeli, M. Bauza, L. P. Kaelbling, J. B. Tenenbaum, and A. Rodriguez, “Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing,” in IROS , 2018, pp. 3066–3073
2018
Earlier work this paper cites.
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine, “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” in RSS , 2018
2018
Earlier work this paper cites.
T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband, G. Dulac-Arnold, J. Agapiou, J. Z. Leibo, and A. Gruslys, “Deep q-learning from demonstrations,” in AAAI , 2018
2018
Cited alongside, same era.
A. Nair, B. McGrew, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Overcoming exploration in reinforcement learning with demonstrations,” in ICRA , 2018, pp. 6292–6299
2018
Cited alongside, same era.
Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, et al. , “Reinforcement and imitation learning for diverse visuomotor skills,” in RSS , 2018
2018
Cited alongside, same era.
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine, “Scalable deep reinforcement learning for vision-based robotic manipulation,” CoRL , 2018
2018
Cited alongside, same era.
M. Barekatain, R. Yonetani, and M. Hamaya, “Multipolar: Multi-source policy aggregation for transfer reinforcement learning between diverse environmental dynamics,” IJCAI , 2020
2020
Later among the works it cites.
G. Schoettler, A. Nair, J. Luo, S. Bahl, J. A. Ojea, E. Solowjow, and S. Levine, “Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards,” IROS , pp. 5548–5555, 2020
2020
Later among the works it cites.
K. Rana, B. Talbot, V. Dasagi, M. Milford, and N. Sünderhauf, “Residual reactive navigation: Combining classical and learned navigation strategies for deployment in unknown environments,” ICRA , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, pp. 421–436, 2018
2018
Cited alongside, same era.
A. Abdolmaleki, J. T. Springenberg, Y. Tassa, R. Munos, N. Heess, and M. Riedmiller, “Maximum a posteriori policy optimisation,” in ICLR , 2018
2018
Cited alongside, same era.
G. Barth-Maron, M. W. Hoffman, D. Budden, W. Dabney, D. Horgan, D. Tb, A. Muldal, N. Heess, and T. Lillicrap, “Distributed distributional deterministic policy gradients,” ICLR , 2018
2018
Cited alongside, same era.
T. Johannink, S. Bahl, A. Nair, J. Luo, A. Kumar, M. Loskyll, J. A. Ojea, E. Solowjow, and S. Levine, “Residual reinforcement learning for robot control,” in ICRA , 2019, pp. 6023–6029
2019
Cited alongside, same era.
A. Zeng, S. Song, J. Lee, A. Rodriquez, and T. A. Funkouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,” in RSS , 2019
2019
Cited alongside, same era.
A. Allevato, E. S. Short, M. Pryor, and A. L. Thomaz, “Tunenet: One-shot residual tuning for system identification and sim-to-real robot task transfer,” CoRL , 2019
2019
Cited alongside, same era.
D. Jain, A. Li, S. Singhal, A. Rajeswaran, V. Kumar, and E. Todorov, “Learning deep visuomotor policies for dexterous hand manipulation,” in ICRA , 2019, pp. 3636–3643
2019
Cited alongside, same era.
M. Neunert, A. Abdolmaleki, M. Wulfmeier, T. Lampe, T. Springenberg, R. Hafner, F. Romano, J. Buchli, N. Heess, and M. Riedmiller, “Continuous-discrete reinforcement learning for hybrid control in robotics,” in CoRL , 2019
2019
Cited alongside, same era.
J. O. Zhang, A. Sax, A. Zamir, L. Guibas, and J. Malik, “Side-tuning: Network adaptation via additive side networks,” ECCV , 2020
2020
Later among the works it cites.
Ç. Gülçehre, Z. Wang, A. Novikov, T. L. Paine, S. G. Colmenarejo, K. Zolna, R. Agarwal, J. Merel, D. J. Mankowitz, C. Paduraru, G. Dulac-Arnold, J. Li, M. Norouzi, M. Hoffman, O. Nachum, G. Tucker, N. Heess, and N. de Freitas, “RL unplugged: A suite of benchmarks for offline reinforcement learning,” NeurIPS , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Wang, A. Novikov, K. Żołna, J. T. Springenberg, S. Reed, B. Shahriari, N. Siegel, J. Merel, C. Gulcehre, N. Heess, and N. de Freitas, “Critic regularized regression,” NeurIPS , 2020
2020
Later among the works it cites.
R. Strudel, A. Pashevich, I. Kalevatykh, I. Laptev, J. Sivic, and C. Schmid, “Learning to combine primitive skills: A step towards versatile robotic manipulation,” in ICRA , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Argenson and G. Dulac-Arnold, “Model-based offline planning,” ICLR , 2021
2021
Closest in time.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” http://pybullet.org , 2016–2021
2021
Closest in time.