Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Chen, Lingyun Zhao, Xinrui de Souza Campanha, Marcos Paulo Wegener, Alexander Naceri, Abdeldjallil Swikir, Abdalla Haddadin, Sami
Chen, Lingyun
Zhao, Xinrui
de Souza Campanha, Marcos Paulo
Wegener, Alexander
Naceri, Abdeldjallil
Swikir, Abdalla
Haddadin, Sami
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Robotics
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Conference proceeding
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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.
Citation
L. Chen, X. Zhao, M.P. de Souza Campanha, A. Wegener, A. Naceri, A. Swikir, S. Haddadin, "Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation," 2025, pp. 9940-9947.
Source
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Conference
International Conference on Intelligent Robots and Systems (IROS)
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence
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Source
International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE