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The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data.
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Deisenroth, M. P., Neumann, G., and Peters, J. (2013) · 2013
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Tools for simulating humanoid robot dynamics: A survey based on user feedback
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Martin-Martin, R. and Brock, O. (2014) · 2014
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Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., et al. (2014) · 2014
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Learning inverse dynamics models with contacts
Calandra, R., Ivaldi, S., Deisenroth, M. P., Rueckert, E., and Peters, J. (2015) · 2015
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Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015) · 2015
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Efficient reinforcement learning for robots using informative simulated priors
Tunenet: One-shot residual tuning for system identification and sim-to-real robot task transfer
Allevato, A., Short, E. S., Pryor, M., and Thomaz, A. (2019) · 2019
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Antonova, R., Rai, A., Li, T., and Kragic, D. (2019) · 2019
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A survey on policy search algorithms for learning robot controllers in a handful of trials
Chatzilygeroudis, K. I., Vassiliades, V., Stulp, F., Calinon, S., and Mouret, J. (2020) · 2019
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Collins, J., Howard, D., and Leitner, J. (2019) · 2019
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Cutler, M. and How, J. P. (2015) · 2015
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Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ODE and physx
Erez, T., Tassa, Y., and Todorov, E. (2015) · 2015
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Fundamental limits on adversarial robustness
Fawzi, A., Fawzi, O., and Frossard, P. (2015) · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
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Distilling the knowledge in a neural network
Hinton, G. E., Vinyals, O., and Dean, J. (2015) · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., et al. (2015) · 2015
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Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids
Mordatch, I., Lowrey, K., and Todorov, E. (2015) · 2015
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Distilling policy distillation
Czarnecki, W. M., Pascanu, R., Osindero, S., Jayakumar, S. M., Swirszcz, G., and Jaderberg, M. (2019) · 2019
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A differentiable physics engine for deep learning in robotics
Degrave, J., Hermans, M., Dambre, J., and Wyffels, F. (2019) · 2019
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Automatic posterior transformation for likelihood-free inference
Greenberg, D. S., Nonnenmacher, M., and Macke, J. H. (2019) · 2019
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Greydanus, S., Dzamba, M., and Yosinski, J. (2019) · 2019
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Chainqueen: A real-time differentiable physical simulator for soft robotics
Hu, Y., Liu, J., Spielberg, A., Tenenbaum, J. B., Freeman, W. T., Wu, J., et al. (2019) · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A. (2019) · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
James, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., et al. (2019) · 2019
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Self-paced contextual reinforcement learning
Klink, P., Abdulsamad, H., Belousov, B., and Peters, J. (2019) · 2019
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Deep lagrangian networks: Using physics as model prior for deep learning
Lutter, M., Ritter, C., and Peters, J. (2019) · 2019
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Active domain randomization
Mehta, B., Diaz, M., Golemo, F., Pal, C. J., and Paull, L. (2019) · 2019
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Sim-to-(multi)-real: Transfer of low-level robust control policies to multiple quadrotors
Molchanov, A., Chen, T., Hönig, W., Preiss, J. A., Ayanian, N., and Sukhatme, G. S. (2019) · 2019
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Learning to plan hierarchically from curriculum
Morere, P., Ott, L., and Ramos, F. (2019) · 2019
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Muratore, F., Gienger, M., and Peters, J. (2021b) · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
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Papamakarios, G., Sterratt, D. C., and Murray, I. (2019) · 2019
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Paul, S., Osborne, M. A., and Whiteson, S. (2019) · 2019
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Language models are unsupervised multitask learners
[Dataset] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019) · 2019
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Bayessim: Adaptive domain randomization via probabilistic inference for robotics simulators
Ramos, F., Possas, R., and Fox, D. (2019) · 2019
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Ruiz, N., Schulter, S., and Chandraker, M. (2019) · 2019
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Sim-to-real transfer for biped locomotion
Yu, W., Kumar, V. C. V., Turk, G., and Liu, C. K. (2019a) · 2019
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Baker, B., Kanitscheider, I., Markov, T. M., Wu, Y., Powell, G., McGrew, B., et al. (2020) · 2020
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DISCO: double likelihood-free inference stochastic control
Barcelos, L., Oliveira, R., Possas, R., Ott, L., and Ramos, F. (2020) · 2020
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Tiny differentiable simulator
[Dataset] Coumans, E. (2020) · 2020
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Durkan, C., Murray, I., and Papamakarios, G. (2020) · 2020
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Kadian, A., Truong, J., Gokaslan, A., Clegg, A., Wijmans, E., Lee, S., et al. (2020) · 2020
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Kaspar, M., Osorio, J. D. M., and Bock, J. (2020) · 2020
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Inferring the material properties of granular media for robotic tasks
Matl, C., Narang, Y. S., Bajcsy, R., Ramos, F., and Fox, D. (2020) · 2020
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A user‘s guide to calibrating robotics simulators
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