Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization

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.

Accepted to the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022)

References in corpus (1)

• Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming

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• Covariance Steering for Uncertain Contact-rich Systems
• Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization
• Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
• Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations