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Robots operating in human environments need various skills, like slow and fast walking, turning, side-stepping, and many more.
M. H. Raibert, Legged robots that balance . MIT press, 1986
1986
Earlier work this paper cites.
L. Jalics, H. Hemami, and Y.-F. Zheng, “Pattern generation using coupled oscillators for robotic and biorobotic adaptive periodic movement,” in ICRA , 1997
1997
Earlier work this paper cites.
W. J. Schwind, Spring loaded inverted pendulum running: A plant model . University of Michigan, 1998
1998
Earlier work this paper cites.
S. Kajita, F. Kanehiro, K. Kaneko, K. Fujiwara, K. Yokoi, and H. Hirukawa, “A realtime pattern generator for biped walking,” in ICRA , vol. 1. IEEE, 2002, pp. 31–37
2002
Earlier work this paper cites.
N. Hansen, “The cma evolution strategy: a comparing review,” Towards a new evolutionary computation , pp. 75–102, 2006
2006
Earlier work this paper cites.
J. Kober and J. Peters, “Learning motor primitives for robotics,” in ICRA . IEEE, 2009, pp. 2112–2118
2009
Earlier work this paper cites.
N. U. Schaller, B. Herkner, R. Villa, and P. Aerts, “The intertarsal joint of the ostrich (struthio camelus): anatomical examination and function of passive structures in locomotion,” Journal of anatomy , vol. 214, no. 6, pp. 830–847, 2009
2009
Earlier work this paper cites.
P. Kormushev, S. Calinon, and D. G. Caldwell, “Robot motor skill coordination with em-based reinforcement learning,” in 2010 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2010, pp. 3232–3237
2010
Earlier work this paper cites.
A. Ude, A. Gams, T. Asfour, and J. Morimoto, “Task-specific generalization of discrete and periodic dynamic movement primitives,” IEEE Transactions on Robotics , vol. 26, no. 5, pp. 800–815, 2010
2010
Earlier work this paper cites.
P. Pastor, M. Kalakrishnan, S. Chitta, E. Theodorou, and S. Schaal, “Skill learning and task outcome prediction for manipulation,” in ICRA . IEEE, 2011, pp. 3828–3834
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: learning attractor models for motor behaviors,” Neural computation , vol. 25, no. 2, pp. 328–373, 2013
2013
Earlier work this paper cites.
K. Mülling, J. Kober, O. Kroemer, and J. Peters, “Learning to select and generalize striking movements in robot table tennis,” The International Journal of Robotics Research , vol. 32, no. 3, pp. 263–279, 2013
2013
Earlier work this paper cites.
A. Wu and H. Geyer, “The 3-d spring–mass model reveals a time-based deadbeat control for highly robust running and steering in uncertain environments,” IEEE TRO , vol. 29, no. 5, pp. 1114–1124, 2013
2013
Earlier work this paper cites.
J. Rosado, F. Silva, and V. Santos, “Adaptation of robot locomotion patterns with dynamic movement primitives,” in 2015 IEEE International Conference on Autonomous Robot Systems and Competitions , 2015, pp. 23–28
2015
Earlier work this paper cites.
H.-W. Park, P. M. Wensing, and S. Kim, “High-speed bounding with the mit cheetah 2: Control design and experiments,” IJRR , vol. 36, no. 2, pp. 167–192, 2017
2017
Cited alongside, same era.
D. Holden, T. Komura, and J. Saito, “Phase-functioned neural networks for character control,” ACM Transactions on Graphics (TOG) , vol. 36, no. 4, pp. 1–13, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Rai, G. Sutanto, S. Schaal, and F. Meier, “Learning feedback terms for reactive planning and control,” in ICRA . IEEE, 2017, pp. 2184–2191
2017
Cited alongside, same era.
2019
Later among the works it cites.
A. Conkey and T. Hermans, “Active learning of probabilistic movement primitives,” in Humanoids . IEEE, 2019, pp. 1–8
2019
Later among the works it cites.
E. Coumans and Y. Bai, “Pybullet,” http://pybullet.org
2019
Later among the works it cites.
2020
Later among the works it cites.
J. Won, D. Gopinath, and J. Hodgins, “A scalable approach to control diverse behaviors for physically simulated characters,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 33–1, 2020
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2017
Cited alongside, same era.
X. B. Peng, P. Abbeel, S. Levine, and M. van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Transactions on Graphics (TOG) , vol. 37, no. 4, pp. 1–14, 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 IROS . IEEE, 2018, pp. 2245–2252
2018
Cited alongside, same era.
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 IROS . IEEE, 2018, pp. 1–9
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 IROS , 2018
2018
Cited alongside, same era.
H. Zhang, S. Starke, T. Komura, and J. Saito, “Mode-adaptive neural networks for quadruped motion control,” ACM Transactions on Graphics (TOG) , vol. 37, no. 4, pp. 1–11, 2018
2018
Cited alongside, same era.
S. Park, H. Ryu, S. Lee, S. Lee, and J. Lee, “Learning predict-and-simulate policies from unorganized human motion data,” ACM Transactions on Graphics (TOG) , vol. 38, no. 6, 2019
2019
Cited alongside, same era.
K. Bergamin, S. Clavet, D. Holden, and J. R. Forbes, “Drecon: Data-driven responsive control of physics-based characters,” ACM Transactions on Graphics (TOG) , vol. 38, no. 6, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
Y.-S. Luo, J. H. Soeseno, T. P.-C. Chen, and W.-C. Chen, “Carl: Controllable agent with reinforcement learning for quadruped locomotion,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
L. Fussell, K. Bergamin, and D. Holden, “Supertrack: motion tracking for physically simulated characters using supervised learning,” ACM Transactions on Graphics (TOG) , vol. 40, no. 6, pp. 1–13, 2021
2021
Closest in time.
D. Kang, S. Zimmermann, and S. Coros, “Animal gaits on quadrupedal robots using motion matching and model based control,” in IROS . IEEE, 2021
2021
Closest in time.
I. Exarchos, Y. Jiang, W. Yu, and C. K. Liu, “Policy transfer via kinematic domain randomization and adaptation,” 2021
2021
Closest in time.
K. Green, Y. Godse, J. Dao, R. L. Hatton, A. Fern, and J. Hurst, “Learning spring mass locomotion: Guiding policies with a reduced-order model,” IEEE RAL , vol. 6, no. 2, pp. 3926–3932, 2021
2021
Closest in time.
2021
Closest in time.
T. Li, R. Calandra, D. Pathak, Y. Tian, F. Meier, and A. Rai, “Planning in learned latent action spaces for generalizable legged locomotion,” IEEE RA: , 2021
2021
Closest in time.