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

-- 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. Navigating complex environments remains a key challenge in robotics, particularly in scenarios requiring enhanced decision-making and adaptability. While most current research emphasizes obstacle avoidance by treating all obstacles as rigid entities to be avoided entirely [1]-[3], relatively little attention has been given to environments containing deformable regions that could be incorporated into the robot's path planning.