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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2504.14103 |
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| _version_ | 1866912737116815360 |
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| author | Atasever, Merve Okhovat, Ali Nazaripouya, Azhang Nisbet, John Kurkutlu, Omer Deshmukh, Jyotirmoy V. Aydin, Yasemin Ozkan |
| author_facet | Atasever, Merve Okhovat, Ali Nazaripouya, Azhang Nisbet, John Kurkutlu, Omer Deshmukh, Jyotirmoy V. Aydin, Yasemin Ozkan |
| contents | Sprawling locomotion in vertebrates, particularly salamanders, demonstrates how body undulation and spinal mobility enhance stability, maneuverability, and adaptability across complex terrains. While prior work has separately explored biologically inspired gait design or deep reinforcement learning (DRL), these approaches face inherent limitations: open-loop gait designs often lack adaptability to unforeseen terrain variations, whereas end-to-end DRL methods are data-hungry and prone to unstable behaviors when transferring from simulation to real robots. We propose a hybrid control framework that integrates Hildebrand's biologically grounded gait design with DRL, enabling a salamander-inspired quadruped robot to exploit active spinal joints for robust crawling motion. Our evaluation across multiple robot configurations in target-directed navigation tasks reveals that this hybrid approach systematically improves robustness under environmental uncertainties such as surface irregularities. By bridging structured gait design with learning-based methodology, our work highlights the promise of interdisciplinary control strategies for developing efficient, resilient, and biologically informed spinal actuation in robotic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility Atasever, Merve Okhovat, Ali Nazaripouya, Azhang Nisbet, John Kurkutlu, Omer Deshmukh, Jyotirmoy V. Aydin, Yasemin Ozkan Robotics Artificial Intelligence Sprawling locomotion in vertebrates, particularly salamanders, demonstrates how body undulation and spinal mobility enhance stability, maneuverability, and adaptability across complex terrains. While prior work has separately explored biologically inspired gait design or deep reinforcement learning (DRL), these approaches face inherent limitations: open-loop gait designs often lack adaptability to unforeseen terrain variations, whereas end-to-end DRL methods are data-hungry and prone to unstable behaviors when transferring from simulation to real robots. We propose a hybrid control framework that integrates Hildebrand's biologically grounded gait design with DRL, enabling a salamander-inspired quadruped robot to exploit active spinal joints for robust crawling motion. Our evaluation across multiple robot configurations in target-directed navigation tasks reveals that this hybrid approach systematically improves robustness under environmental uncertainties such as surface irregularities. By bridging structured gait design with learning-based methodology, our work highlights the promise of interdisciplinary control strategies for developing efficient, resilient, and biologically informed spinal actuation in robotic systems. |
| title | Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.14103 |