The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control
Open accessAbstract
This paper presents an implementation of model predictive control (MPC) to determine ground reaction forces for a torque-controlled quadruped robot. The robot dynamics are simplified to formulate the problem as convex optimization while still capturing the full 3D nature of the system. With the simplified model, ground reaction force planning problems are formulated for prediction horizons of up to 0.5 seconds, and are solved to optimality in under 1 ms at a rate of 20-30 Hz. Despite using a simplified model, the robot is capable of robust locomotion at a variety of speeds. Experimental results demonstrate control of gaits including stand, trot, flying-trot, pronk, bound, pace, a 3-legged gait, and a full 3D gallop. The robot achieved forward speeds of up to 3 m/s, lateral speeds up to 1 m/s, and angular speeds up to 180 deg/sec. Our approach is general enough to perform all these behaviors with the same set of gains and weights.
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Fast Model Predictive Control Using Online Optimization
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qpOASES: a parametric active-set algorithm for quadratic programming
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ECOS: An SOCP solver for embedded systems
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An online active set strategy to overcome the limitations of explicit MPC
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Self-citationWinkler, A. W., Bellicoso, C. D., Hutter, M., & Buchli, J. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2798285 · Cited by 451
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Cited by
Mini Cheetah: A Platform for Pushing the Limits of Dynamic Quadruped Control
Katz, B., Carlo, J. D., & Kim, S. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793865 · Cited by 544
Self-citationLearning to Walk Via Deep Reinforcement Learning
Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2019) · Robotics: Science and Systems XV · doi:10.15607/rss.2019.xv.011 · Cited by 430
Learning Agile Robotic Locomotion Skills by Imitating Animals
Bin Peng, X., Coumans, E., Zhang, T., Lee, T.-W., Tan, J., & Levine, S. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.064 · Cited by 357
CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion
Bellegarda, G., & Ijspeert, A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3218167 · Cited by 125
Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion
Sun, Y., Ubellacker, W. L., Ma, W.-L., Zhang, X., Wang, C., Csomay-Shanklin, N. V., Tomizuka, M., Sreenath, K., & Ames, A. D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3108510 · Cited by 54
Metadata
- Authors
- Di Carlo, J., Wensing, P. M., Katz, B., Bledt, G., & Kim, S.
- Venue
- 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 1-9
- OpenAlex
- W2909331752
- Cited by
- 758
- References
- 21
- FWCI
- 80.46
- Type
- conference paper
- Language
- en
- Open access
- green
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- Di Carlo, J., Wensing, P. M., Katz, B., Bledt, G., & Kim, S. (2018). Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2018.8594448
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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.
