Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization

TL;DR

This paper introduces a robust optimization framework for pivoting manipulation that leverages frictional properties to enhance stability and robustness against uncertainties in physical object parameters.

Contribution

It develops a Contact Implicit Bilevel Optimization method that maximizes stability margin through analytical insights, improving manipulation robustness under uncertainty.

Findings

Analytical expressions for stability margin based on friction.

Optimized trajectories that enhance robustness during pivoting.

Validated control strategy with a 6 DoF manipulator.

Abstract

Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this paper, we study robust optimization for planning of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for inaccuracies in the estimates of the physical properties during manipulation. Under certain assumptions, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a Contact Implicit Bilevel Optimization (CIBO) framework to optimize a trajectory that maximizes this stability margin to provide robustness against uncertainty in several physical parameters of the object. We present analysis of the stability margin with respect to several parameters involved in the underlying bilevel optimization problem. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects. We also design and validate an MPC controller using the proposed algorithm which can track and regulate the position of the object during manipulation.