系统梳理多指机器人手灵巧操作评测方法，提出分层评估框架。
Benchmarking Dexterity of Multifingered Robot Hands: A Review and Perspective
- 构建三级渐进式评测框架，涵盖从基础到复杂任务
- 聚焦手内精细操作，填补现有评测体系空白
- 适合机器人操控、人机交互领域研究者参考
机器人手是人工智能与物理世界的关键接口，提升机器人灵巧性对实现物理智能至关重要。尽管简单夹持器已展现出色灵巧性，多指手在操作多样性、精度和适应性方面具有更大潜力。本文综述了多指机器人手灵巧性评测的最新进展，基于美国国家科学基金会手工程研究中心的观点，特别关注手内精细操作。提出一个包含三个层级的评测框架，对应系统复杂度递增，回顾各层级代表性基准，同时提出新基准与评估指标以解决文献中的局限性。更多信息详见 https://hand-erc.github.io/benchmarking/。
原文摘要 · Abstract (English)
Robot hands are a key interface between AI and the physical world, making advances in robotic dexterity essential to realizing the vision of physical AI. While impressive dexterity has been demonstrated with simple grippers, multifingered hands offer the potential for substantially greater versatility, precision, and adaptability in manipulation. In this review, we survey the state of the art in benchmarking the dexterity of multifingered robot hands. Recognizing dexterity as a complex and multifaceted concept, we present the perspective of the U.S. National Science Foundation HAND Engineering Research Center, with a particular focus on fine in-hand manipulation. We introduce a framework consisting of three benchmark levels that correspond to increasing system complexity, review representative benchmarks at each level, and propose new benchmarks and metrics to address limitations in the literature. More information can be found at https://hand-erc.github.io/benchmarking/.
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