Contact-Implicit Planning and Control for Non-prehensile Manipulation Using State-Triggered Constraints

Wang, Maozhen; Onol, Aykut Ozgun; Long, Philip; Padır, Taşkın

doi:10.1007/978-3-031-25555-7_13

Cited by 1 publication

(1 citation statement)

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“…Direct methods introduce decision variables for both state and control, and enforce system dynamics with constraints [10][11][12][13][14][15]. This leads to a large but sparse program that can be solved by general-purpose optimizers like SNOPT [16] and IPOPT [17].…”

Section: A Offline Contact-implicit Planningmentioning

confidence: 99%

Contact-Implicit Trajectory Optimization with Hydroelastic Contact and iLQR

Kurtz

Lin

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Robots must make and break contact to interact with the world and perform useful tasks. However, planning and control through contact remains a formidable challenge. In this work, we achieve real-time contactimplicit model predictive control with a surprisingly simple method: inverse dynamics trajectory optimization. While trajectory optimization with inverse dynamics is not new, we introduce a series of incremental innovations that collectively enable fast model predictive control on a variety of challenging manipulation and locomotion tasks. We implement these innovations in an open-source solver, and present a variety of simulation examples to support the effectiveness of the proposed approach. Additionally, we demonstrate contact-implicit model predictive control on hardware at over 100 Hz for a 20 degree-of-freedom bimanual manipulation task.

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