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Playing the guitar is a dexterous human skill that poses significant challenges in computer graphics and robotics due to the precision required in finger positioning and coordination between hands. Current methods often rely on motion capture data to replicate specific guitar playing segments, which restricts the range of performances and demands intricate post‐processing. In this paper, we introduce a novel reinforcement learning model that can play the guitar using robotic hands, without the need for motion capture datasets, from input tablatures. To achieve this, we divide the simulation task for playing guitar into three stages. (a): for an input tablature, we first generate corresponding fingerings that align with human habits. (b): based on the generated fingerings as the guidance, we train a neural network for controlling the fingers of the left hand using deep reinforcement learning, and (c): we generate plucking movements for the right hand based on inverse kinematics according to the tablature. We evaluate our method by employing precision, recall, and F1 scores as quantitative metrics to thoroughly assess its performance in playing musical notes. In addition, we conduct qualitative analysis through user studies to evaluate the visual and auditory effects of guitar performance. The results demonstrate that our model excels in playing most moderately difficult and easier musical pieces, accurately playing nearly all notes. [ABSTRACT FROM AUTHOR]

We present a method for computational modeling, mechanical characterization, and macro-scale simulation of discrete interlocking materials (DIM)---3D-printed chainmail fabrics made of quasi-rigid interlocking elements. Unlike conventional elastic materials for which deformation and restoring force are directly coupled, the mechanics of DIM are governed by contacts between individual elements that give rise to anisotropic deformation constraints. To model the mechanical behavior of these materials, we propose a computational approach that builds on three key components. (a): we explore the space of feasible deformations using native-scale simulations at the per-element level. (b): based on this simulation data, we introduce the concept of strain-space boundaries to represent deformation limits for in- and out-of-plane deformations, and (c): we use the strain-space boundaries to drive an efficient macro-scale simulation model based on homogenized deformation constraints. We evaluate our method on a set of representative discrete interlocking materials and validate our findings against measurements on physical prototypes. [ABSTRACT FROM AUTHOR]