Fetching the paper…
Reading the bibliography…
A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints and provide interpretability of the sequence of commands over the horizon.
G. Williams, A. Aldrich, and E. Theodorou, “Model predictive path integral control using covariance variable importance sampling,” 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
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
Earlier work this paper cites.
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018
2018
Earlier work this paper cites.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,”
2019
Earlier work this paper cites.
H. Li and P. M. Wensing, “Hybrid systems differential dynamic programming for whole-body motion planning of legged robots,”
2020
Earlier work this paper cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,”
2020
Earlier work this paper cites.
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” 2020
2020
Earlier work this paper cites.
Y. Ding, A. Pandala, C. Li, Y.-H. Shin, and H.-W. Park, “Representation-free model predictive control for dynamic motions in quadrupeds,”
2021
Cited alongside, same era.
A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,”
2021
Cited alongside, same era.
T. Zong, L. Sun, and Y. Liu, “Reinforced ilqr: A sample-efficient robot locomotion learning,” in
2021
Cited alongside, same era.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance gpu-based physics simulation for robot learning,” 2021
2021
Cited alongside, same era.
A. S. Morgan, D. Nandha, G. Chalvatzaki, C. D’Eramo, A. M. Dollar, and J. Peters, “Model predictive actor-critic: Accelerating robot skill acquisition with deep reinforcement learning,” in
2021
R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter, “Perceptive locomotion through nonlinear model-predictive control,”
2023
Later among the works it cites.
H. Li, T. Zhang, W. Yu, and P. M. Wensing, “Versatile real-time motion synthesis via kino-dynamic mpc with hybrid-systems ddp,” in
2023
Later among the works it cites.
G. B. Margolis and P. Agrawal, “Walk these ways: Tuning robot control for generalization with multiplicity of behavior,” in
2023
Later among the works it cites.
I. M. A. Nahrendra, B. Yu, and H. Myung, “Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,” in
2023
Later among the works it cites.
X. Cheng, K. Shi, A. Agarwal, and D. Pathak, “Extreme parkour with legged robots,”
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,”
2022
Cited alongside, same era.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in
2022
Cited alongside, same era.
P. Wu, A. Escontrela, D. Hafner, K. Goldberg, and P. Abbeel, “Daydreamer: World models for physical robot learning,”
2022
Cited alongside, same era.
H. Sikchi, W. Zhou, and D. Held, “Learning off-policy with online planning,” in
2022
Cited alongside, same era.
N. Hansen, X. Wang, and H. Su, “Temporal difference learning for model predictive control,”
2022
Cited alongside, same era.
U. A. Mishra, S. R. Samineni, P. Goel, C. Kunjeti, H. Lodha, A. Singh, A. Sagi, S. Bhatnagar, and S. Kolathaya, “Dynamic mirror descent based model predictive control for accelerating robot learning,” in
2022
Cited alongside, same era.
Later among the works it cites.
Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” in
2023
Later among the works it cites.
N. Hansen, H. Su, and X. Wang, “Td-mpc2: Scalable, robust world models for continuous control,”
2023
Later among the works it cites.
J. Zhu, Y. Wang, L. Wu, T. Qin, W. gang Zhou, T.-Y. Liu, and H. Li, “Making better decision by directly planning in continuous control,” in
2023
Later among the works it cites.
J. Long, Z. Wang, Q. Li, L. Cao, J. Gao, and J. Pang, “Hybrid internal model: Learning agile legged locomotion with simulated robot response,” in
2024
Closest in time.
D. Hoeller, N. Rudin, D. Sako, and M. Hutter, “Anymal parkour: Learning agile navigation for quadrupedal robots,”
2024
Closest in time.
Nilaksh, A. Ranjan, S. Agrawal, A. Jain, P. Jagtap, and S. Kolathaya, “Barrier functions inspired reward shaping for reinforcement learning,” in
2024
Closest in time.