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Page 286                        Zhang et al. Intell Robot 2022;2(3):275­97  I http://dx.doi.org/10.20517/ir.2022.20


                   Los Angeles, CA, USA, July 28­30, 2017. Eurographics Association / ACM; 2017. pp. 12:1–2:13. Avaialble from: https://doi.org/10.1
                   145/3099564.3099567 [Last accessed on 30 Aug 2022].
               44.  Chen S, Zhang B, Mueller MW, Rai A, Sreenath K. Learning torque control for quadrupedal locomotion. CoRR 2022;abs/2203.05194.
                   Avaialble from: https://doi.org/10.48550/arXiv.2203.05194 [Last accessed on 30 Aug 2022].
               45.  Carlo JD, Wensing PM, Katz B, Bledt G, Kim S. Dynamic locomotion in the MIT cheetah 3 through convex model­predictive control.
                   In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, October 1­5, 2018. IEEE;
                   2018. pp. 1–9. Avaialble from: https://doi.org/10.1109/IROS.2018.8594448 [Last accessed on 30 Aug 2022].
               46.  Jain D, Iscen A, Caluwaerts K. Hierarchical reinforcement learning for quadruped locomotion. In: 2019 IEEE/RSJ International Con­
                   ference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3­8, 2019. IEEE; 2019. pp. 7551–57. Avaialble
                   from: https://doi.org/10.1109/IROS40897.2019.8967913 [Last accessed on 30 Aug 2022].
               47.  Li T, Lambert NO, Calandra R, Meier F, Rai A. Learning generalizable locomotion skills with hierarchical reinforcement learning. In:
                   2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 ­ August 31, 2020. IEEE; 2020.
                   pp. 413–19. Avaialble from: https://doi.org/10.1109/ICRA40945.2020.9196642 [Last accessed on 30 Aug 2022].
               48.  Lee J, Hwangbo J, Hutter M.  Robust recovery controller for a quadrupedal robot using deep reinforcement learning.  CoRR
                   2019;abs/1901.07517. Available from: http://arxiv.org/abs/1901.07517 [Lasta accessed on 30 Aug 2022].
               49.  Iscen A, Caluwaerts K, Tan J, et al. Policies modulating trajectory generators. In: 2nd Annual Conference on Robot Learning, CoRL
                   2018, Zürich, Switzerland, 29­31 October 2018, Proceedings. vol. 87 of Proceedings of Machine Learning Research. PMLR; 2018. pp.
                   916–26. Available from: http://proceedings.mlr.press/v87/iscen18a.html [last accessed on 30 Aug 2022].
               50.  Rahme M, Abraham I, Elwin ML, Murphey TD. Dynamics and domain randomized gait modulation with Bezier curves for sim­to­real
                   legged locomotion. CoRR 2020;abs/2010.12070. Avaialble from: https://arxiv.org/abs/2010.12070 [Last accessed on 30 Aug 2022].
               51.  Zhang H, Wang J, Wu Z, Wang Y, Wang D. Terrain­aware risk­assessment­network­aided deep reinforcement learning for quadrupedal
                   locomotion in tough terrain. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech
                   Republic, September 27 ­ Oct. 1, 2021. IEEE; 2021. pp. 4538–45. Avaialble from: https://doi.org/10.1109/IROS51168.2021.9636519
                   [Last accessed on 30 Aug 2022].
               52.  Yang Y, Zhang T, Coumans E, Tan J, Boots B. Fast and efficient locomotion via learned gait transitions. In: Faust A, Hsu D, Neumann
                   G, editors. Conference on Robot Learning, 8­11 November 2021, London, UK. vol. 164 of Proceedings of Machine Learning Research.
                   PMLR; 2021. pp. 773–83. Avaialble from: https://proceedings.mlr.press/v164/yang22d.html [Last accessed on 30 Aug 2022].
               53.  Gangapurwala S, Geisert M, Orsolino R, Fallon MF, Havoutis I. Real­time trajectory adaptation for quadrupedal locomotion using deep
                   reinforcement learning. In: IEEE International Conference on Robotics and Automation, ICRA 2021, Xi’an, China, May 30 ­ June 5,
                   2021. IEEE; 2021. pp. 5973–79. Avaialble from: https://doi.org/10.1109/ICRA48506.2021.9561639 [Last accessed on 30 Aug 2022].
               54.  Yao Q, Wang J, Wang D, et al. Hierarchical terrain­aware control for quadrupedal locomotion by combining deep reinforcement learning
                   and optimal control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech Repub­
                   lic, September 27 ­ Oct. 1, 2021. IEEE; 2021. pp. 4546–51. Avaialble from: https://doi.org/10.1109/IROS51168.2021.9636738 [Last
                   accessed on 30 Aug 2022].
               55.  Singla A, Bhattacharya S, Dholakiya D, et al. Realizing learned quadruped locomotion behaviors through kinematic motion primitives. In:
                   International Conference on Robotics and Automation, ICRA 2019, Montreal, QC, Canada, May 20­24, 2019. IEEE; 2019. pp. 7434–40.
                   Avaialble from: https://doi.org/10.1109/ICRA.2019.8794179 [Last accessed on 30 Aug 2022].
               56.  Vollenweider E, Bjelonic M, Klemm V, et al. Advanced skills through multiple adversarial motion priors in reinforcement learning. arXiv
                   preprint arXiv:220314912 2022. DOI
               57.  Li A, Wang Z, Wu J, Zhu Q. Efficient learning of control policies for robust quadruped bounding using pretrained neural networks. arXiv
                   preprint arXiv:201100446 2020. DOI
               58.  Shao Y, Jin Y, Liu X, et al. Learning free gait transition for quadruped robots via phase­guided controller. IEEE Robotics and Automation
                   Letters 2021;7:1230–37. DOI
               59.  Luo J, Hauser KK. Robust trajectory optimization under frictional contact with iterative learning. Auton Robots 2017;41:1447–61.
                   Avaialble from: https://doi.org/10.1007/s10514­017­9629­x [Last accessed on 30 Aug 2022].
               60.  Kumar A, Fu Z, Pathak D, Malik J. RMA: rapid motor adaptation for legged robots. In: Proceedings of Robotics: Science and Systems.
                   Virtual; 2021. DOI
               61.  Liu J, Zhang H, Wang D. DARA: dynamics­aware reward augmentation in offline reinforcement learning. CoRR 2022;abs/2203.06662.
                   Avaialble from: https://doi.org/10.48550/arXiv.2203.06662 [Last accessed on 30 Aug 2022].
               62.  Shi H, Zhou B, Zeng H, et al. Reinforcement learning with evolutionary trajectory generator: A general approach for quadrupedal
                   locomotion. IEEE Robotics Autom Lett 2022;7:3085–92. Avaialble from: https://doi.org/10.1109/LRA.2022.3145495 [Last accessed on
                   30 Aug 2022].
               63.  Zhao W, Queralta JP, Westerlund T. Sim­to­real transfer in deep reinforcement learning for robotics: a survey. In: 2020 IEEE Symposium
                   Series on Computational Intelligence, SSCI 2020, Canberra, Australia, December 1­4, 2020. IEEE; 2020. pp. 737–44. Avaialble from:
                   https://doi.org/10.1109/SSCI47803.2020.9308468 [Last accessed on 30 Aug 2022].
               64.  Mnih V, Kavukcuoglu K, Silver D, et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:13125602 2013. DOI
               65.  Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O. Proximal policy optimization algorithms. arXiv preprint arXiv:170706347
                   2017. DOI
               66.  Wang C, Yang T, Hao J, et al. ED2: an environment dynamics decomposition framework for world model construction. CoRR
                   2021;abs/2112.02817. Avaialble from: https://arxiv.org/abs/2112.02817 [Last accessed on 30 Aug 2022].
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