The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
CHOMP: Covariant Hamiltonian optimization for motion planning
Open accessAbstract
In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using seven-degree-of-freedom manipulators and a rough-terrain quadruped robot.
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Citations
Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Khatib, O. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500106 · Cited by 7,894
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343 · cited 1×
“, 1997) methods were shown to be well suited for path planning in C-spaces with many degrees of freedom (DOFs) (Kavraki et al., 1996), and with complex constraints, including kinodynamic (Kuffner, 1999; Hsu, 2000; Casal, 2001; Kindel, 2001).”
Method
Cited by
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
Zhou, B., Gao, F., Wang, L., Liu, C., & Shen, S. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2927938 · Cited by 747
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Self-citationKwon, M., Huang, S. H., & Dragan, A. D. (2018) · Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3171221.3171276 · Cited by 123
Self-citationMotions in Microseconds via Vectorized Sampling-Based Planning
Thomason, W., Kingston, Z., & Kavraki, L. E. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611190 · Cited by 28
Metadata
- Authors
- Zucker, M., Ratliff, N., Dragan, A. D., Pivtoraiko, M., Klingensmith, M., Dellin, C. M., Bagnell, J. A., & Srinivasa, S. S.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2013-08
- Volume
- 32
- Issue
- 9-10
- Pages
- 1164-1193
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2161819990
- Cited by
- 758
- References
- 3
- FWCI
- 32.71
- Type
- journal article
- Language
- en
- Open access
- green
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- Zucker, M., Ratliff, N., Dragan, A. D., Pivtoraiko, M., Klingensmith, M., Dellin, C. M., Bagnell, J. A., & Srinivasa, S. S. (2013). CHOMP: Covariant Hamiltonian optimization for motion planning. The International Journal of Robotics Research. https://doi.org/10.1177/0278364913488805
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