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Legged robots have the potential to traverse complex terrain and access confined spaces beyond the reach of traditional platforms thanks to their ability to carefully select footholds and flexibly adapt their body posture while walking.
J. Z. Kolter, Y. Kim, and A. Y. Ng, “Stereo vision and terrain modeling for quadruped robots,” in 2009 IEEE International Conference on Robotics and Automation . IEEE, 2009, pp. 1557–1564
2009
Earlier work this paper cites.
M. Zucker, J. A. Bagnell, C. G. Atkeson, and J. Kuffner, “An optimization approach to rough terrain locomotion,” in 2010 IEEE International Conference on Robotics and Automation . IEEE, 2010, pp. 3589–3595
2010
Earlier work this paper cites.
P. D. Neuhaus, J. E. Pratt, and M. J. Johnson, “Comprehensive summary of the institute for human and machine cognition’s experience with LittleDog,” The International Journal of Robotics Research , vol. 30, no. 2, pp. 216–235, 2011
2011
Earlier work this paper cites.
I. Havoutis, J. Ortiz, S. Bazeille, V. Barasuol, C. Semini, and D. G. Caldwell, “Onboard perception-based trotting and crawling with the hydraulic quadruped robot (HyQ),” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 6052–6057
2013
Earlier work this paper cites.
A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: an efficient probabilistic 3d mapping framework based on octrees.” Auton. Robots , vol. 34, no. 3, pp. 189–206, 2013. [Online]. Available: http://dblp.uni-trier.de/db/journals/arobots/arobots34.html#HornungWBSB13
2013
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto, “Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017
2017
Earlier work this paper cites.
P. Fankhauser, M. Bloesch, and M. Hutter, “Probabilistic terrain mapping for mobile robots with uncertain localization,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3019–3026, 2018
2018
Earlier work this paper cites.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” in Robotics: Science and Systems , 2018
2018
Earlier work this paper cites.
J. Hwangbo, J. Lee, and M. Hutter, “Per-contact iteration method for solving contact dynamics,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 895–902, 2018. [Online]. Available: www.raisim.com
2018
Earlier work this paper cites.
R. Buchanan, T. Bandyopadhyay, M. Bjelonic, L. Wellhausen, M. Hutter, and N. Kottege, “Walking posture adaptation for legged robot navigation in confined spaces,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 2148–2155, 2019
2019
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,” Sci Robot , vol. 4, no. 26, Jan. 2019
2019
Earlier work this paper cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, 2020
2020
Earlier work this paper cites.
A. Bouman, M. F. Ginting, N. Alatur, M. Palieri, D. D. Fan, T. Touma, T. Pailevanian, S.-K. Kim, K. Otsu, J. Burdick, and A.-A. Agha-mohammadi, “Autonomous spot: Long-Range autonomous exploration of extreme environments with legged locomotion,” Oct. 2020
2020
Earlier work this paper cites.
D. Kim, D. Carballo, J. Di Carlo, B. Katz, G. Bledt, B. Lim, and S. Kim, “Vision aided dynamic exploration of unstructured terrain with a small-scale quadruped robot,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2464–2470
2020
Cited alongside, same era.
F. Jenelten, T. Miki, A. E. Vijayan, M. Bjelonic, and M. Hutter, “Perceptive locomotion in rough terrain–online foothold optimization,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5370–5376, 2020
2020
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” Robotics: Science and Systems , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
F. Jenelten, R. Grandia, F. Farshidian, and others, “TAMOLS: Terrain-aware motion optimization for legged systems,” IEEE Transactions on , 2022
2022
Later among the works it cites.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 91–100
2022
Later among the works it cites.
D. Hoeller, N. Rudin, C. Choy, and others, “Neural scene representation for locomotion on structured terrain,” IEEE Robotics and , 2022
2022
Later among the works it cites.
C. S. Imai, M. Zhang, Y. Zhang, M. Kierebiński, R. Yang, Y. Qin, and X. Wang, “Vision-Guided quadrupedal locomotion in the wild with Multi-Modal delay randomization,” pp. 5556–5563, Oct. 2022
2022
Later among the works it cites.
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R. Buchanan, L. Wellhausen, M. Bjelonic, T. Bandyopadhyay, N. Kottege, and M. Hutter, “Perceptive whole-body planning for multilegged robots in confined spaces,” Journal of Field Robotics , vol. 38, no. 1, pp. 68–84, 2021
2021
Cited alongside, same era.
J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst, “Blind bipedal stair traversal via sim-to-real reinforcement learning,” in Robotics: Science and Systems , 2021
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 Gavriel State, “Isaac gym: High performance GPU-Based physics simulation for robot learning,” Aug. 2021
2021
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,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abk2822
2022
Cited alongside, same era.
M. Tranzatto, M. Dharmadhikari, L. Bernreiter, M. Camurri, S. Khattak, F. Mascarich, P. Pfreundschuh, D. Wisth, S. Zimmermann, M. Kulkarni, V. Reijgwart, B. Casseau, T. Homberger, P. De Petris, L. Ott, W. Tubby, G. Waibel, H. Nguyen, C. Cadena, R. Buchanan, L. Wellhausen, N. Khedekar, O. Andersson, L. Zhang, T. Miki, T. Dang, M. Mattamala, M. Montenegro, K. Meyer, X. Wu, A. Briod, M. Mueller, M. Fallon, R. Siegwart, M. Hutter, and K. Alexis, “Team CERBERUS wins the DARPA subterranean challenge: Technical overview and lessons learned,” Jul. 2022
2022
Cited alongside, same era.
M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart, and K. Alexis, “CERBERUS in the DARPA subterranean challenge,” Sci Robot , vol. 7, no. 66, p. eabp9742, May 2022
2022
Cited alongside, same era.
R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang, “Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=nhnJ3oo6AB
2022
Cited alongside, same era.
W. Yu, D. Jain, A. Escontrela, A. Iscen, P. Xu, E. Coumans, S. Ha, J. Tan, and T. Zhang, “Visual-Locomotion: Learning to walk on complex terrains with vision,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 2022, pp. 1291–1302
2022
Cited alongside, same era.
P. Arm, G. Waibel, J. Preisig, T. Tuna, R. Zhou, V. Bickel, G. Ligeza, T. Miki, F. Kehl, H. Kolvenbach et al. , “Scientific exploration of challenging planetary analog environments with a team of legged robots,” Science robotics , vol. 8, no. 80, p. eade9548, 2023
2023
Later among the works it cites.
N. Kottege, J. Williams, B. Tidd, F. Talbot, R. Steindl, M. Cox, D. Frousheger, T. Hines, A. Pitt, B. Tam, B. Wood, L. Hanson, K. Lo Surdo, T. Molnar, M. Wildie, K. Stepanas, G. Catt, L. Tychsen-Smith, D. Penfold, L. Overs, M. Ramezani, K. Khosoussi, F. Kendoul, G. Wagner, D. Palmer, J. Manderson, C. Medek, M. O’Brien, S. Chen, and R. C. Arkin, “Heterogeneous robot teams with unified perception and autonomy: How team CSIRO data61 tied for the top score at the DARPA subterranean challenge,” Feb. 2023
2023
Later among the works it cites.
L. Han, Q. Zhu, J. Sheng, C. Zhang, T. Li, Y. Zhang, H. Zhang, Y. Liu, C. Zhou, R. Zhao, J. Li, Y. Zhang, R. Wang, W. Chi, X. Li, Y. Zhu, L. Xiang, X. Teng, and Z. Zhang, “Lifelike agility and play on quadrupedal robots using reinforcement learning and generative pre-trained models,” Aug. 2023
2023
Later among the works it cites.
Z. Zhuang, Z. Fu, J. Wang, C. G. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” in 7th Annual Conference on Robot Learning , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Kim, H. Oh, J. Lee, J. Choi, G. Ji, M. Jung, D. Youm, and J. Hwangbo, “Not only rewards but also constraints: Applications on legged robot locomotion,” Aug. 2023
2023
Later among the works it cites.
R. Yang, G. Yang, and X. Wang, “Neural volumetric memory for visual locomotion control,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1430–1440
2023
Later among the works it cites.
G. Erni, J. Frey, T. Miki, M. Mattamala, and M. Hutter, “Mem: Multi-modal elevation mapping for robotics and learning,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 11 011–11 018
2023
Later among the works it cites.
R. Grandia, F. Jenelten, S. Yang, and others, “Perceptive locomotion through nonlinear Model-Predictive control,” IEEE Transactions , 2023
2023
Later among the works it cites.