Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization

TL;DR

This paper introduces a bilevel optimization approach to enhance the robustness of pivoting manipulation by exploiting frictional stability, enabling robots to better handle uncertainties in object properties.

Contribution

It develops an analytical framework for friction-based stability margins and integrates it into a bilevel trajectory optimization for robust pivoting control.

Findings

The method improves manipulation stability under physical uncertainties.

Analytical expressions quantify friction's role in stability margins.

Experimental validation on a 6 DoF robot demonstrates effectiveness.

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 interaction with uncertainty in physical properties of the object. In this paper, we study robust optimization for control of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for the inaccuracies in the estimates of the physical properties during manipulation. In particular, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a bilevel trajectory optimization algorithm to design a controller that maximizes this stability margin to provide robustness against uncertainty in physical properties of the object. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects.