LAUSR

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.