The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Learning, planning, and control for quadruped locomotion over challenging terrain
Abstract
We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization, and control techniques to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal foothold choices from expert demonstration using terrain templates , (2) a body trajectory optimizer based on the Zero-Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller by testing it on the LittleDog quadruped robot, over a wide variety of rough terrains of varying difficulty levels. The terrain that the robot was tested on includes rocks, logs, steps, barriers, and gaps, with obstacle sizes up to the leg length of the robot. We demonstrate the generalization ability of this controller by presenting results from testing performed by an independent external test team on terrain that has never been shown to us.
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Cites
A note on two problems in connexion with graphs
Dijkstra, E. W. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,043
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 15,539
Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 11,726
Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890
Optimizing search engines using clickthrough data
Joachims, T. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775067 · Cited by 3,873
Mangasarian, O. L. (1994) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611971255 · Cited by 3,220
Proceedings of 1995 IEEE International Conference on Robotics and Automation
(1995) · Proceedings of 1995 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1995.525251 · Cited by 2,830
Raibert, M. H., & Tello, E. R. (1986) · IEEE Expert · doi:10.1109/mex.1986.4307016 · Cited by 2,725
Weik, M. H. (2000) · Computer Science and Communications Dictionary · doi:10.1007/1-4020-0613-6_12436 · Cited by 2,570
Cited by
Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds
Neunert, M., Stauble, M., Giftthaler, M., Bellicoso, C. D., Carius, J., Gehring, C., Hutter, M., & Buchli, J. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800124 · Cited by 284
Self-citationHyun, D. J., Seok, S., Lee, J., & Kim, S. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364914532150 · Cited by 269
High-speed bounding with the MIT Cheetah 2: Control design and experiments
Park, H.-W., Wensing, P. M., & Kim, S. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917694244 · Cited by 267
From motor control to team play in simulated humanoid football
Liu, S., Lever, G., Wang, Z., Merel, J., Eslami, S. M. A., Hennes, D., Czarnecki, W. M., Tassa, Y., Omidshafiei, S., Abdolmaleki, A., et al. (2022) · Science Robotics · doi:10.1126/scirobotics.abo0235 · Cited by 88
An application of sequential composition control to cooperative systems
Najafi, E., Babuska, R., & Lopes, G. A. D. (2015) · 2015 10th International Workshop on Robot Motion and Control (RoMoCo) · doi:10.1109/romoco.2015.7219707 · Cited by 15
Metadata
- Authors
- Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., & Schaal, S.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2010-11-12
- Volume
- 30
- Issue
- 2
- Pages
- 236-258
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2154139861
- Cited by
- 294
- References
- 44
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
