Performance Characterization of the LEAP Hand: Control Interface and Digital Twin Integration

Recent advances in machine learning (ML) techniques have spurred the development of efficient robotic grasping models. However, approaches such as reinforcement learning (RL) require extensive training time, posing significant challenges from a hardware perspective. To address these, cost-effective robotic platforms have emerged, facilitating affordable experimentation and rapid prototyping. In this work, we focus on the LEAP Hand — a low-cost, dexterous, and anthropomorphic robotic hand. Despite its promising capabilities, a comprehensive characterization of its control aspects is currently lacking, limiting our understanding of its mechanical and operational constraints. To bridge this gap, we introduce a user-friendly tool designed for testing the LEAP Hand across various configurations. Our interface enhances pose management by incorporating user-defined motion control, serving as an effective debugging aid. Additionally, we present an integration of our control interface with the GPU-based physics simulator Isaac Sim, which lays the foundation for deploying advanced, complex movements to the hand. We validate our approach through long-term movement experiments, ranging from 35 minutes to 4 hours, and analyze performance in terms of movement precision, dynamic tracking behavior, and thermal characteristics. Our findings offer valuable insights for defining efficient control strategies, optimizing motion profiles, and improving response times in dynamic tasks.