TR2023-061
Chance-Constrained Optimization in Contact-rich Systems
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- "Chance-Constrained Optimization in Contact-rich Systems", American Control Conference (ACC), DOI: 10.23919/ACC55779.2023.10156516, May 2023, pp. 14-21.BibTeX TR2023-061 PDF
- @inproceedings{Shirai2023may4,
- author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
- title = {Chance-Constrained Optimization in Contact-rich Systems},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- pages = {14--21},
- month = may,
- publisher = {IEEE},
- doi = {10.23919/ACC55779.2023.10156516},
- url = {https://www.merl.com/publications/TR2023-061}
- }
,
- "Chance-Constrained Optimization in Contact-rich Systems", American Control Conference (ACC), DOI: 10.23919/ACC55779.2023.10156516, May 2023, pp. 14-21.
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Abstract:
This paper presents a chance-constrained formu- lation for robust trajectory optimization during manipulation. In particular, we present a chance-constrained optimization for Stochastic Discrete-time Linear Complementarity Systems (SDLCS). To solve the optimization problem, we formulate Mixed-Integer Quadratic Programming with Chance Con- straints (MIQPCC). In our formulation, we explicitly consider joint chance constraints for complementarity as well as states to capture the stochastic evolution of dynamics. We evaluate robustness of our optimized trajectories in simulation on several systems. The proposed approach outperforms some recent approaches for robust trajectory optimization for SDLCS.