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

arXiv: https://arxiv.org/abs/2506.20376 · HTML: https://mlsanity.com/paper/2506.20376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 4 of 4)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces](https://mlsanity.com/paper/2211.08199/) (2022)
- [Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments](https://mlsanity.com/paper/2402.00537/) (2024)

## Abstract (arXiv)

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.