Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation

TL;DR

This paper introduces a novel robot navigation method that combines Learning from Demonstration with Dynamical Systems, utilizing a dynamic modulation matrix to adaptively navigate complex deformable environments safely and efficiently.

Contribution

It presents a new integration of LfD and DS with real-time dynamic modulation for deformable obstacle navigation, enhancing adaptability and safety.

Findings

Successful simulation and robot experiments demonstrate effective navigation in deformable environments.

The method maintains trajectory fidelity while dynamically avoiding obstacles.

Control over trajectory and velocity improves interaction with deformable objects.

Abstract

This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (DS), we enable adaptive and efficient navigation in complex environments where obstacles consist of both soft and hard regions. We introduce a dynamic modulation matrix within the DS framework, allowing the system to distinguish between traversable soft regions and impassable hard areas in real-time, ensuring safe and flexible trajectory planning. We validate our method through extensive simulations and robot experiments, demonstrating its ability to navigate deformable environments. Additionally, the approach provides control over both trajectory and velocity when interacting with deformable objects, including at intersections, while maintaining adherence to the original DS trajectory and dynamically adapting to obstacles for smooth and reliable navigation.