Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

X. B. Peng, M. Chang, G. Zhang, P. Abbeel, and S. Levine, “Mcp: Learning composable hierarchical control with multiplicative compositional policies,” CoRR , vol. abs/1905.09808, 2019. [Online]. Available: http://arxiv.org/abs/1905.09808

Original

1905

Earlier work this paper cites.

R. S. Sutton, D. Precup, and S. Singh, “Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,” Artificial intelligence , vol. 112, no. 1-2, pp. 181–211, 1999

1999

Earlier work this paper cites.

D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica , vol. 36, no. 6, pp. 789–814, 2000

2000

Earlier work this paper cites.

H. Kimura, Y. Fukuoka, and A. H. Cohen, “Biologically inspired adaptive walking of a quadruped robot,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , vol. 365, no. 1850, pp. 153–170, 2006

2006

Earlier work this paper cites.

A. Crespi, D. Lachat, A. Pasquier, and A. J. Ijspeert, “Controlling swimming and crawling in a fish robot using a central pattern generator,” Autonomous Robots , vol. 25, no. 1-2, pp. 3–13, 2008

2008

Earlier work this paper cites.

A. Herdt, H. Diedam, P.-B. Wieber, D. Dimitrov, K. Mombaur, and M. Diehl, “Online walking motion generation with automatic footstep placement,” Advanced Robotics , vol. 24, no. 5-6, pp. 719–737, 2010

2010

Earlier work this paper cites.

F. Stulp and S. Schaal, “Hierarchical reinforcement learning with movement primitives,” in 2011 11th IEEE-RAS International Conference on Humanoid Robots . IEEE, 2011, pp. 231–238

2011

Earlier work this paper cites.

C. Daniel, G. Neumann, O. Kroemer, and J. Peters, “Learning sequential motor tasks,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 2626–2632

2013

Earlier work this paper cites.

Y. Fukuoka, Y. Habu, and T. Fukui, “Analysis of the gait generation principle by a simulated quadruped model with a cpg incorporating vestibular modulation,” Biological cybernetics , vol. 107, no. 6, pp. 695–710, 2013

2013

Earlier work this paper cites.

H.-W. Park, P. M. Wensing, S. Kim et al. , “Online planning for autonomous running jumps over obstacles in high-speed quadrupeds,” 2015

2015

Earlier work this paper cites.

J. Koenemann, A. Del Prete, Y. Tassa, E. Todorov, O. Stasse, M. Bennewitz, and N. Mansard, “Whole-body model-predictive control applied to the hrp-2 humanoid,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 3346–3351

2015

Earlier work this paper cites.

J. André, C. Teixeira, C. P. Santos, and L. Costa, “Adapting biped locomotion to sloped environments,” Journal of Intelligent & Robotic Systems , vol. 80, no. 3-4, pp. 625–640, 2015

2015

Earlier work this paper cites.

A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret, “Robots that can adapt like animals,” Nature , vol. 521, no. 7553, pp. 503–507, 2015

2015

Earlier work this paper cites.

J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in International conference on machine learning , 2015, pp. 1889–1897

2015

Cited alongside, same era.

S. Feng, “Online hierarchical optimization for humanoid control,” 2016

2016

Cited alongside, same era.

C. Daniel, H. Van Hoof, J. Peters, and G. Neumann, “Probabilistic inference for determining options in reinforcement learning,” Machine Learning , vol. 104, no. 2-3, pp. 337–357, 2016

2016

Cited alongside, same era.

P.-L. Bacon, J. Harb, and D. Precup, “The option-critic architecture,” 2016

2016

Cited alongside, same era.

X. B. Peng, G. Berseth, and M. van de Panne, “Terrain-adaptive locomotion skills using deep reinforcement learning,” ACM Trans. Graph. , vol. 35, no. 4, pp. 81:1–81:12, Jul. 2016. [Online]. Available: http://doi.acm.org/10.1145/2897824.2925881

T. Haarnoja, A. Zhou, S. Ha, J. Tan, G. Tucker, and S. Levine, “Learning to walk via deep reinforcement learning,” arXiv preprint arXiv:1812.11103 , 2018

Original

2018

Later among the works it cites.

S. Mason, N. Rotella, S. Schaal, and L. Righetti, “An mpc walking framework with external contact forces,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1785–1790

2018

Later among the works it cites.

B. Yang, G. Wang, R. Calandra, D. Contreras, S. Levine, and K. Pister, “Learning flexible and reusable locomotion primitives for a microrobot,” IEEE Robotics and Automation Letters (RA-L) , vol. 3, no. 3, pp. 1904–1911, 2018

2018

Later among the works it cites.

T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel et al. , “Soft actor-critic algorithms and applications,” arXiv preprint arXiv:1812.05905 , 2018

Original

2018

Later among the works it cites.