Dyadic collaborative Manipulation through Hybrid Trajectory Optimization

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Theodoros Stouraitis, Iordanis Chatzinikolaidis, Michael Gienger, Sethu Vijayakumar ;
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:869-878, 2018.

Abstract

This work provides a principled formalism to address the joint planning problem in dyadic collaborative manipulation (DcM) scenarios by representing the human’s intentions as task space forces and solving the joint problem holistically via model-based optimization. The proposed method is the first to empower robotic agents with the ability to plan in hybrid spaces—optimizing over discrete contact locations, continuous trajectory and force profiles, for co-manipulation tasks with varied dyadic objective goals. This ability is particularly important in large object manipulation scenarios that typically require change of grasp-holds. The task of finding the contact points, forces and the respective timing of grasp-hold changes are carried out by a joint optimization using non-linear solvers. We demonstrate the efficacy of the optimization method by investigating the effect of robot policy changes (trajectories, timings, grasp-holds) based on changes in collaborative partner policies using physically based dynamic simulations. We also realize, in hardware, effective co-manipulation of a large object by the human and the robot, including eminent grasp changes as well as optimal dyadic interactions to realize the joint task.

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