From Obstacle Avoidance To Motion Learning Using Local Rotation of Dynamical Systems

TL;DR

This paper introduces a novel approach to robotic motion control by describing movements relative to the robot itself using local rotations, enabling obstacle avoidance and motion learning with promising experimental results.

Contribution

It proposes a new framework for obstacle avoidance and motion learning based on local rotations of dynamical systems, shifting from external to internal motion descriptions.

Findings

Effective obstacle avoidance for star-shaped obstacles.

Successful application to LASA handwriting dataset.

Framework enables region-specific dynamics learning.

Abstract

In robotics motion is often described from an external perspective, i.e., we give information on the obstacle motion in a mathematical manner with respect to a specific (often inertial) reference frame. In the current work, we propose to describe the robotic motion with respect to the robot itself. Similar to how we give instructions to each other (go straight, and then after multiple meters move left, and then a sharp turn right.), we give the instructions to a robot as a relative rotation. We first introduce an obstacle avoidance framework that allows avoiding star-shaped obstacles while trying to stay close to an initial (linear or nonlinear) dynamical system. The framework of the local rotation is extended to motion learning. Automated clustering defines regions of local stability, for which the precise dynamics are individually learned. The framework has been applied to the LASA-handwriting dataset and shows promising results.