A Learning Method for Stiffness Control of a Drum Robot for Rebounding Double Strokes

Abstract

In robot drumming, performing double stroke rolls is a key ability. Human drummers learn to play double strokes by just trying it several times. For performing it, a model needs to be learned to provide anticipatory commands during drumming. Joint stiffness plays a key role in rebounding double stroke task and should be considered in the model. We have introduced an interactive learning method for a drum robot to learn joint stiffness for rebounding double stroke task. The model is simulated for a 2-DoF robotic arm. The algorithm is simulated with 3 different drum kits to show the robustness of the learning approach. The simulation results also show significant compatibility with human performance results. In addition, the refined learning algorithm adjusts the stroke timing which is important for producing proper rhythms.

Robotic Arm Generative Painting Through Real-time Analysis of Music Performance

“…Recent approaches have focused on techniques such as using Brushless Direct Current (BLDC) motors for high speed and dynamic range [14]. Other projects have focused on adding extra degrees of freedom, allowing for variation in where the drum is hit [15] or adding a flexible joint to the system to improve drum roll performance [16].…”

Analyzing Drumming Technique with Trajectory Optimization

“…As a result, different percussion groups performing at comparable levels have adopted distinguished approaches to achieving the same basic goal of precisely controlling the motion of the bead of the drumstick to strike the drumhead at a desired time with a desired force [3,4] . While some quantitative analyzes of drumming systems have been performed either to better understand the system experimentally [5,6] or to reproduce a controllable drumming motion with a robot [7,8] , the optimal drumming technique, or control policy, for driving the bead of a drumstick to a given state while minimizing the effort of the percussionist remains to be quantitatively investigated. Furthermore, the tools for completing such an investigation are not currently accessible to nonexperts of chaotic multibody dynamic systems and optimal control theory.…”

“…* Corresponding author at: Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh 15213, PA, USA. E-mail address: opearl@andrew.cmu.edu (O. Pearl); ORCID: 0000-0001-5926-0577 While some quantitative analyzes of drumming systems have been performed either to better understand the system experimentally [5,6] or to reproduce a controllable drumming motion with a robot [7,8] , the optimal drumming technique, or control policy, for driving the bead of a drumstick to a given state while minimizing the effort of the percussionist remains to be quantitatively investigated. Furthermore, the tools for completing such an investigation are not currently accessible to nonexperts of chaotic multibody dynamic systems and optimal control theory.…”