Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation

Abstract

We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hardcontact 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.

A Holistic Approach to Human-Supervised Humanoid Robot Operations in Extreme Environments

“…Yet, they consider only tabletop pushing scenarios. Moreover, in general, experimental results in this domain are very limited, albeit with some notable exceptions (Mordatch et al, 2015;Mastalli et al, 2016;Neunert et al, 2017Neunert et al, , 2018Giftthaler et al, 2017;Carius et al, 2018;Winkler et al, 2018;Sleiman et al, 2019). Nonetheless, to the best of our knowledge, there is no experimental verification of CITO for constrained dynamic manipulation.…”

“…On the other hand, in (Mordatch et al, 2012a;Posa et al, 2014;Gabiccini et al, 2018), manipulation tasks are investigated but their analyses are either limited to a planar case or based on animated characters where physical fidelity is not critical. Recently , 2020Sleiman et al, 2019), used CITO for non-prehensile manipulation tasks. Yet, they consider only tabletop pushing scenarios.…”

Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints

“…Recent works [6], [15]- [17] have developed TO methods for planning robot manipulation tasks requiring contact changes. The underlying formulation of these works follows one of two classes: contact-implicit [18] or multi-mode [6], [19].…”

“…Another way of expressing different modes in hybrid systems is to use complementarity constraints, which have recently been widely used in many TO based planning methods for robotics [8], [9], [17], [18]. The complementarity constraints remove the need for using integer variables in the optimization problem.…”

“…Particular to the pusher-slider problem, the key insight for exploring complementarity constraints instead of a mixed integer formulation is that, even though the dynamics are hybrid, the transition between the different modes is continuous, meaning that the system can transition from the currently active contact mode to any other contact mode, at any instant in time. Note that for the scenario of making and breaking of contact [8], [17], [18], when far away from contact it is physically infeasible to instantaneously transition to contact. This additional guard condition ensuring that mode transitions only occur from certain state space regions is nonexistent in our pusher-slider problem.…”