Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints
This work addresses contact adaptation in robotic manipulation, offering incremental improvements in planning and control efficiency for specific planar tasks.
The paper tackled the problem of non-prehensile planar manipulation by developing a trajectory optimization method using complementarity constraints to switch between sticking and sliding contact modes, showing that it converges faster, scales better, and achieves better tracking compared to mixed integer alternatives in numerical and experimental validations.
Contact adaption is an essential capability when manipulating objects. Two key contact modes of non-prehensile manipulation are sticking and sliding. This paper presents a Trajectory Optimization (TO) method formulated as a Mathematical Program with Complementarity Constraints (MPCC), which is able to switch between these two modes. We show that this formulation can be applicable to both planning and Model Predictive Control (MPC) for planar manipulation tasks. We numerically compare: (i) our planner against a mixed integer alternative, showing that the MPCC planer converges faster, scales better with respect to time horizon, and can handle environments with obstacles; (ii) our controller against a state-of-the-art mixed integer approach, showing that the MPCC controller achieves better tracking and more consistent computation times. Additionally, we experimentally validate both our planner and controller with the KUKA LWR robot on a range of planar manipulation tasks.