HiFun: a Hierarchical Framework for Efficient Functional Dexterous Manipulation Learning

While multi-fingered dexterous robot hands have the potential to provide human-level dexterity, using them for functional manipulation remains difficult because task success depends on precise contact, coordinated arm-hand motion, and reactive recovery from execution errors. Precise contact depends on complex contact dynamics that are difficult to model accurately in simulation, motivating real-world learning.

Aus dem Abstract. Unsere Zusammenfassung folgt.