Fetching the paper…
Reading the bibliography…
Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME–Journal of Basic Engineering
1960
Earlier work this paper cites.
J. Schmidhuber, “Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook,” diploma thesis, Technische Universitat Munchen, Germany, 1987
1987
Earlier work this paper cites.
S. Hochreiter, A. S. Younger, and P. R. Conwell, “Learning to learn using gradient descent,” in ICANN
2001
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in IROS
2012
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR
2015
Earlier work this paper cites.
T. Haarnoja, A. Ajay, S. Levine, and P. Abbeel, “Backprop kf: Learning discriminative deterministic state estimators,” in NIPS
2016
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016
2016
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 IROS
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in ICML
2017
Earlier work this paper cites.
A. Ghadirzadeh, A. Maki, D. Kragic, and M. Björkman, “Deep predictive policy training using reinforcement learning,” in IROS
2017
Earlier work this paper cites.
M. Hazara and V. Kyrki, “Speeding up incremental learning using data efficient guided exploration,” in ICRA
2018
Cited alongside, same era.
B. Stadie, G. Yang, R. Houthooft, P. Chen, Y. Duan, Y. Wu, P. Abbeel, and I. Sutskever, “The importance of sampling in meta-reinforcement learning,” in NIPS
2018
Cited alongside, same era.
C. Finn, K. Xu, and S. Levine, “Probabilistic model-agnostic meta-learning,” in NIPS
2018
Cited alongside, same era.
J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn, “Bayesian model-agnostic meta-learning,” in NIPS
2018
Cited alongside, same era.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” in RSS
2018
Cited alongside, same era.
M. Hazara and V. Kyrki, “Transferring generalizable motor primitives from simulation to real world,” IEEE RA-L
2019
Later among the works it cites.
K. Arndt, M. Hazara, A. Ghadirzadeh, and V. Kyrki, “Meta reinforcement learning for sim-to-real domain adaptation,” in ICRA
2019
Later among the works it cites.
A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn, “Learning to adapt in dynamic, real-world environments through meta-reinforcement learning,” in ICLR
2019
Later among the works it cites.
P. A. Ortega et al., “Meta-learning of sequential strategies,” tech. rep., DeepMind, 2019
2019
Later among the works it cites.
P. Becker, H. Pandya, G. Gebhardt, C. Zhao, C. Taylor, and G. Neumann, “Recurrent kalman networks: factorized inference in high-dimensional deep feature spaces,” in ICML
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Sadeghi, A. Toshev, E. Jang, and S. Levine, “Sim2real view invariant visual servoing by recurrent control,” in CVPR
2018
Cited alongside, same era.
A. Gupta, R. Mendonca, Y. Liu, P. Abbeel, and S. Levine, “Meta-reinforcement learning of structured exploration strategies,” in NIPS
2018
Cited alongside, same era.
I. Clavera, J. Rothfuss, J. Schulman, Y. Fujita, T. Asfour, and P. Abbeel, “Model-based reinforcement learning via meta-policy optimization,” in CoRL
2018
Cited alongside, same era.
C. Finn and S. Levine, “Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm,” in ICLR
2018
Cited alongside, same era.
A. Hämäläinen, K. Arndt, A. Ghadirzadeh, and V. Kyrki, “Affordance learning for end-to-end visuomotor robot control,” in IROS
2019
Cited alongside, same era.
J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. E. Turner, “Meta-learning probabilistic inference for prediction,” in ICLR
2019
Later among the works it cites.
J. Rothfuss, D. Lee, I. Clavera, T. Asfour, and P. Abbeel, “Promp: Proximal meta-policy search,” in ICLR
2019
Later among the works it cites.
OpenAI, “Learning dexterous in-hand manipulation,” IJRR
2020
Closest in time.
S. Flennerhag, A. A. Rusu, R. Pascanu, H. Yin, and R. Hadsell, “Meta-learning with warped gradient descent,” in ICLR
2020
Closest in time.
X. Song, Y. Yang, K. Choromanski, K. Caluwaerts, W. Gao, C. Finn, and J. Tan, “Rapidly adaptable legged robots via evolutionary meta-learning,” in IROS
2020
Closest in time.