Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation
This work addresses dynamic object manipulation for robotics, but it is incremental as it builds on existing contact-implicit optimization methods with enhancements for efficiency and physical correctness.
The authors tackled the problem of generating optimal trajectories for robots in contact-rich dynamic manipulation tasks by reformulating a contact-implicit optimization approach with a hard-contact model and Newton's impact law, resulting in increased computational efficiency and verified feasibility through simulations and hardware experiments.
We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hard-contact model is assumed, and the unilateral constraints are imposed in the form of complementarity conditions. Newton's impact law is adopted for enhanced physical correctness. The optimal control problem is formulated as a multi-staged program through a multiple-shooting scheme. This problem structure is exploited within the FORCES Pro framework to retrieve optimal motion plans, contact sequences and control inputs with increased computational efficiency. We investigate our method on a variety of dynamic object manipulation tasks, performed by a six degrees of freedom robot. The dynamic feasibility of the optimal trajectories, as well as the repeatability and accuracy of the task-satisfaction are verified through simulations and real hardware experiments on one of the manipulation problems.