多指ロボットハンドの巧緻性ベンチマーク：レビューと展望
Benchmarking Dexterity of Multifingered Robot Hands: A Review and Perspective
多指ロボットハンドの巧緻性を評価するためのベンチマーク手法を体系的にレビューし、3段階のベンチマークフレームワークを提案する論文。
著者: Anthony Shilati, Anunth Ramaswami, Luke Batteas, Sylvia Tan, Anthony Barcio, Sairam Umakanth, Preksha Rao, David McDougall, Landry Graves, Ahmet A. Ozkan, Yunsoo Yoon, Arushi Pradhan, Michael G. Henry, Rohan Kota, Damian Gonzalez, Gray C. Thomas, Gary K. Fedder, J. Edward Colgate, Kevin M. Lynch
分類: cs.RO
原文アブストラクト
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/.