Fetching the paper…
Reading the bibliography…
Understanding the gap between simulation and reality is critical for reinforcement learning with legged robots, which are largely trained in simulation.
M. McNaughton, “Castro: robust nonlinear trajectory optimization using multiple models,” in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2007, pp. 177–182
2007
Earlier work this paper cites.
C. G. Atkeson, “Efficient robust policy optimization,” in 2012 American Control Conference (ACC) . IEEE, 2012, pp. 5220–5227
2012
Earlier work this paper cites.
H. Dai and R. Tedrake, “Optimizing robust limit cycles for legged locomotion on unknown terrain,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) . IEEE, 2012, pp. 1207–1213
2012
Earlier work this paper cites.
M. Focchi, T. Boaventura, C. Semini, M. Frigerio, J. Buchli, and D. G. Caldwell, “Torque-control based compliant actuation of a quadruped robot,” in 2012 12th IEEE international workshop on advanced motion control (AMC) . IEEE, 2012, pp. 1–6
2012
Earlier work this paper cites.
C. Gehring, S. Coros, M. Hutter, M. Bloesch, M. A. Hoepftinger, and R. Y. Siegwart, “Control of dynamic gaits for a quadrupedal robot,” in IEEE International Conference on Robotics and Automation (ICRA), 2013: 6-10 May 2013, Karlsruhe, Germany . IEEE, 2013, pp. 3287–3292
2013
Earlier work this paper cites.
I. Mordatch, K. Lowrey, and E. Todorov, “Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 5307–5314
2015
Earlier work this paper cites.
B. Thananjeyan, A. Garg, S. Krishnan, C. Chen, L. Miller, and K. Goldberg, “Multilateral surgical pattern cutting in 2d orthotropic gauze with deep reinforcement learning policies for tensioning,” in IEEE International Conference on Robotics and Automation (ICRA) , jun 2017
2017
Earlier work this paper cites.
A. Mandlekar, Y. Zhu, A. Garg, L. Fei-Fei, and S. Savarese, “Adversarially robust policy learning: Active construction of physically-plausible perturbations,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 3932–3939
2017
Earlier work this paper cites.
J. Harrison*, A. Garg*, B. Ivanovic, Y. Zhu, S. Savarese, L. Fei-Fei, and M. Pavone (* equal contribution), “AdaPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems,” in International Symposium on Robotics Research (ISRR) . Springer STAR, dec 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim, “Dynamic locomotion in the mit cheetah 3 through convex model-predictive control,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 1–9
2018
Cited alongside, same era.
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , May 2018, pp. 1–8
2018
Cited alongside, same era.
Z. Xie, G. Berseth, P. Clary, J. Hurst, and M. van de Panne, “Feedback control for cassie with deep reinforcement learning,” in Proc. IEEE/RSJ Intl Conf on Intelligent Robots and Systems (IROS 2018) , 2018
2018
Cited alongside, same era.
G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, and S. Kim, “Mit cheetah 3: Design and control of a robust, dynamic quadruped robot,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 2245–2252
X. B. Peng, E. Coumans, T. Zhang, T.-W. E. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” in Robotics: Science and Systems , 07 2020
2020
Closest in time.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, 2020. [Online]. Available: https://robotics.sciencemag.org/content/5/47/eabc5986
2020
Closest in time.
J. Dao, H. Duan, K. Green, J. Hurst, and A. Fern, “Learning to walk without dynamics randomization,” 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics , 2020
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, 2019
2019
Cited alongside, same era.
Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. van de Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in Proc. Conference on Robot Learning (CORL 2019) , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
T. Li, H. Geyer, C. G. Atkeson, and A. Rai, “Using deep reinforcement learning to learn high-level policies on the atrias biped,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 263–269
2019
Cited alongside, same era.
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox, “Closing the sim-to-real loop: Adapting simulation randomization with real world experience,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8973–8979
2019
Cited alongside, same era.
2020
Closest in time.
S. Gangapurwala, A. Mitchell, and I. Havoutis, “Guided constrained policy optimization for dynamic quadrupedal robot locomotion,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3642–3649, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
NVIDIA, Isaac Gym - Preview Release , 2020. [Online]. Available: https://developer.nvidia.com/isaac-gym
2020
Closest in time.