Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing (2409.16629v2)

Abstract: We present a novel approach to synthesize dexterous motions for physically simulated hands in tasks that require coordination between the control of two hands with high temporal precision. Instead of directly learning a joint policy to control two hands, our approach performs bimanual control through cooperative learning where each hand is treated as an individual agent. The individual policies for each hand are first trained separately, and then synchronized through latent space manipulation in a centralized environment to serve as a joint policy for two-hand control. By doing so, we avoid directly performing policy learning in the joint state-action space of two hands with higher dimensions, greatly improving the overall training efficiency. We demonstrate the effectiveness of our proposed approach in the challenging guitar-playing task. The virtual guitarist trained by our approach can synthesize motions from unstructured reference data of general guitar-playing practice motions, and accurately play diverse rhythms with complex chord pressing and string picking patterns based on the input guitar tabs that do not exist in the references. Along with this paper, we provide the motion capture data that we collected as the reference for policy training. Code is available at: https://pei-xu.github.io/guitar.

• The paper introduces a decentralized approach that trains independent hand policies and synchronizes them in latent space to simplify high-dimensional bimanual control.
• It demonstrates the viability of physics-based virtual guitar playing, achieving a 193% improvement in F1 score for coordinated dual-hand performance.
• The method offers scalable insights for multi-agent tasks with potential applications in VR/AR, robotic manipulation, and advanced musical performance.

Overview of Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing

The paper presents a novel methodology to synthesize dexterous motions for bimanual tasks requiring both hands to interact with high temporal precision, specifically in the context of virtual guitar playing. The authors introduce a cooperative learning framework whereby each hand is treated as an individual agent, and their independent policies are later synchronized through latent space manipulation in a centralized training environment.

Key Contributions

Decoupled Bimanual Policy Learning:

Instead of directly learning a joint policy in the high-dimensional state-action space of both hands, the approach begins with decentralized training where each hand is controlled by an independent policy. The synchronizer later coordinates their behaviors as a joint policy. This decomposition significantly enhances training efficiency by simplifying the initial learning problem.

Guitar Playing Task:

The chosen application for this framework is virtual guitar playing, a task that necessitates intricate coordination between the left hand fretting chords and the right hand strumming or picking strings. The authors evaluate their method's effectiveness by demonstrating the virtual guitarist's ability to play complex rhythms and chords derived from unstructured reference motion capture data and novel guitar tabs.

Synchronization Through Latent Space Manipulation:

The synchronization process involves latent space manipulation to align the policies of both hands. Essentially, the behaviors of the well-trained individual policies are adjusted minimally within this latent space to achieve synchronized performance, thereby avoiding the complexity of retraining in the joint state-action space.

Motion Capture Dataset:

The authors supplement their methodology with a motion capture dataset collected from a professional guitarist, which serves as reference data for policy training. This dataset is publicly available, providing a valuable resource for future research.

Numerical Results and Claims

The experimental evaluation showcases significant improvements in performance after policy synchronization. The dual-hand policies achieve average F1 score improvements of 193% post-synchronization, evidencing the efficacy of the proposed method. Notably, the policies exhibit remarkable proficiency in accurately performing a variety of guitar-playing tasks, including the execution of complex chord patterns and intricate strumming sequences.

Theoretical and Practical Implications

From a theoretical standpoint, the decomposition of the bimanual control problem into separate sub-tasks followed by latent space synchronization presents a scalable solution for other cooperative multi-agent scenarios. This method circumvents the exponential increase in complexity associated with joint policy learning in higher-dimensional spaces.

Practically, the ability to efficiently train virtual agents for tasks necessitating high temporal precision and dexterity, as demonstrated in guitar playing, opens new avenues for virtual performance in VR/AR environments. This can lead to enhanced immersive experiences in virtual reality concerts and interactive music training applications. Additionally, the approach has potential applications in robotic manipulation tasks requiring synchronized dual-arm coordination.

Future Directions

Future work could explore extending the framework to instruments with more complex interaction dynamics, such as violins or harps, and to cooperative tasks beyond musical performance, such as robotic surgery or intricate assembly tasks. There is also potential in improving the motion synthesis fidelity by incorporating more sophisticated hand models and higher temporal resolution simulations. Another exciting direction could focus on enhancing policy generalization across diverse tasks through meta-learning techniques.

In summary, the proposed method for synchronized dual-hand control via cooperative learning and latent space manipulation is a robust approach yielding high-quality, temporally precise bimanual tasks. The virtual guitarist's ability to generalize from unstructured reference data highlights the potential of this framework in various applications, paving the way for future advancements in AI-driven dexterous control.