Robot Learning from Demonstration Using Elastic Maps

TL;DR

This paper introduces a new robot learning from demonstration method that uses elastic maps to encode demonstrations, optimizing for accuracy, trajectory length, and smoothness, and demonstrates its effectiveness in real-world robotic tasks.

Contribution

The paper presents a novel elastic map-based optimization approach for robot learning from demonstration, offering improved flexibility and performance over existing methods.

Findings

Effective in real-world robot tasks with UR5e arm

Outperforms other LfD methods in multiple metrics

Flexible construction and weighting of elastic maps

Abstract

Learning from Demonstration (LfD) is a popular method of reproducing and generalizing robot skills from human-provided demonstrations. In this paper, we propose a novel optimization-based LfD method that encodes demonstrations as elastic maps. An elastic map is a graph of nodes connected through a mesh of springs. We build a skill model by fitting an elastic map to the set of demonstrations. The formulated optimization problem in our approach includes three objectives with natural and physical interpretations. The main term rewards the mean squared error in the Cartesian coordinate. The second term penalizes the non-equidistant distribution of points resulting in the optimum total length of the trajectory. The third term rewards smoothness while penalizing nonlinearity. These quadratic objectives form a convex problem that can be solved efficiently with local optimizers. We examine nine methods for constructing and weighting the elastic maps and study their performance in robotic tasks. We also evaluate the proposed method in several simulated and real-world experiments using a UR5e manipulator arm, and compare it to other LfD approaches to demonstrate its benefits and flexibility across a variety of metrics.