Dexterous In-Hand Manipulation of Slender Cylindrical Objects through Deep Reinforcement Learning with Tactile Sensing

Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of small objects. This work proposed a framework for end-to-end policy learning with tactile feedback and sim-to-real transfer...

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Veröffentlicht in:	arXiv.org 2023-04
Hauptverfasser:	Hu, Wenbin, Huang, Bidan, Wang, Wei Lee, Yang, Sicheng, Zheng, Yu, Li, Zhibin
Format:	Artikel
Sprache:	eng
Schlagworte:	Comparative studies Deep learning Dextrous hands End effectors Policies Sensor arrays Sensory feedback Tactile sensors (robotics)
Online-Zugang:	Volltext
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