On-Line Learning for Planning and Control of Underactuated Robots with Uncertain Dynamics

TL;DR

This paper introduces an iterative online learning method for planning and controlling underactuated robots with uncertain dynamics, enabling efficient trajectory generation and accurate execution despite large uncertainties.

Contribution

It proposes a novel iterative approach that learns and updates model uncertainties in real-time for improved planning and control of underactuated robots.

Findings

Few iterations needed for feasible trajectory generation

Effective tracking control with large model uncertainties

Validated through simulations and Pendubot experiments

Abstract

We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties.