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This study advances soft robotics by applying Bayesian optimization to control elastic dynamics for high-frequency drum rolls, demonstrating that specific motion tricks significantly outperform rigid actuators in acoustic efficiency and bounce consistency. The work highlights the critical role of data-driven tuning in managing nonlinear systems, offering a scalable framework for complex manipulation tasks beyond simple drumming.
Multi-bounce Drum Roll with Optimized Active Tricks to Leverage Soft Embodiment
arXiv:2609.08490v1 Announce Type: new Abstract: This paper presents a soft robotic drummer for accurate and efficient drum rolls. High-frequency drum rolls require the "multi-bounce technique," where a drumstick bounces multiple times with a single stroke. In robotic reproduction of this technique, the body's elasticity is key, while the fine motion during the stroke is also crucial for maximizing the potential of that elasticity. Therefore, we design two tricks: i) Tap-Pull (TP) trick to increase the number of rebounds by adding a pulling motion after impact; and ii) Micro-Pulse (MP) trick to keep the drumming volume by injecting small oscillations during the stroke. Due to the nonlinear complexity of soft embodiment, both tricks are efficiently tuned using Bayesian optimization in a data-driven manner for accomplishing the respective objectives quantified. We evaluated the optimized behaviors with soft and rigid end-effectors. As a result, the soft TP achieved the highest bounce count (12.25 per stroke) with uniform intervals. The soft MP suppressed the volume decay, yielding 6.8-times higher acoustic efficiency compared to the rigid MP. These results indicate that the proposed tricks with the combination of elasticity and optimization can make robots play excellent drum rolls.
Abstract:This paper presents a soft robotic drummer for accurate and efficient drum rolls. High-frequency drum rolls require the "multi-bounce technique," where a drumstick bounces multiple times with a single stroke. In robotic reproduction of this technique, the body's elasticity is key, while the fine motion during the stroke is also crucial for maximizing the potential of that elasticity. Therefore, we design two tricks: i) Tap-Pull (TP) trick to increase the number of rebounds by adding a pulling motion after impact; and ii) Micro-Pulse (MP) trick to keep the drumming volume by injecting small oscillations during the stroke. Due to the nonlinear complexity of soft embodiment, both tricks are efficiently tuned using Bayesian optimization in a data-driven manner for accomplishing the respective objectives quantified. We evaluated the optimized behaviors with soft and rigid end-effectors. As a result, the soft TP achieved the highest bounce count (12.25 per stroke) with uniform intervals. The soft MP suppressed the volume decay, yielding 6.8-times higher acoustic efficiency compared to the rigid MP. These results indicate that the proposed tricks with the combination of elasticity and optimization can make robots play excellent drum rolls.
7 pages, 6 figures. Published in the 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2026)
Robotics (cs.RO)
arXiv:2609.08490 [cs.RO]
(or arXiv:2609.08490v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.08490 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/AIM65483.2026.11658185 Focus to learn more DOI(s) linking to related resources
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