End-to-end from human hand synergies to robot hand tendon routing

Research output: Contribution to journalResearch articleContributed

Contributors

  • Diego X. Hidalgo Carvajal - , Technical University of Munich, Dresden University of Technology (Author)
  • Christopher Herneth - , Technical University of Munich (Author)
  • Abdeldjallil Naceri - , Technical University of Munich (Author)
  • Sami Haddadin - , Technical University of Munich, Dresden University of Technology (Author)

Abstract

The human hand capabilities are paramount for highly dexterous manipulation interactions. Unfortunately, the limitations of current technologies make replicating such capabilities unfeasible. Although several works have focused on directly attempting to create robot hands able to mimic human ones closely, few of them have attempted to create generalizable platforms, where robotic hand mechanisms can be iteratively selected and customized to different tasks. In order to build highly dexterous robotic hands in the future, it is crucial to understand not only human manipulation, but also develop methods to leverage robotic mechanisms limitations to mimic human hand interactions accurately. In this letter, we propose an end-to-end framework capable of generating underactuated tendon routings that allow a generic robot hand model to reproduce desired observed human grasp motion synergies accurately. Our contributions are threefold: (1) an end to end framework to generate task-oriented robot hand tendon routings, with the potential to implement desired synergies, (2) a novel grammar based representation of robot hand tendon routings, and (3) a schematic visualization of robot hand tendon routings. The latter two contributions have the potential to embed and compare properties among robot hands. Our results in simulation show that the proposed method produces tendon routing mechanisms that are able to closely mimic the joint trajectories of human subjects performing the same experimental tasks, while achieving dynamically stable grasping postures.

Details

Original languageEnglish
Pages (from-to)10057–10064
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
Publication statusPublished - 1 Oct 2022
Peer-reviewedNo

External IDs

Scopus 85135203872
Mendeley 9d913b2e-4aa2-3b75-9c41-45ea20ef5c86
unpaywall 10.1109/lra.2022.3192649