Dexterous Object Manipulation with an Anthropomorphic Robot Hand via Natural Hand Pose Transformer and Deep Reinforcement Learning

Dexterous Object Manipulation via Integrated Hand-Arm Systems

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform multiple tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts. Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been show...

Dexterous Anthropomorphic Robot Hand With Distributed Tactile Sensor: Gifu Hand II

This paper presents an anthropomorphic robot hand called the Gifu hand II, which has a thumb and four fingers, all the joints of which are driven by servomotors built into the fingers and the palm. The thumb has four joints with four-degrees-of-freedom (DOF); the other fingers have four joints with 3-DOF; and two axes of the joints near the palm cross orthogonally at one point, as is the case i...

LUCS Haptic Hand III An Anthropomorphic Robot Hand with Proprioception

The Lucs Haptic Hand III has been built as a step in a project at LUCS aiming at studying haptic perception. In this project, several robot hands together with cognitive computational models of the corresponding human neurophysiologic systems will be built. Grasping tests with the LUCS Haptic Hand III were done with six different objects in order to get a comprehension of the signal patterns fr...

Investigating Deep Reinforcement Learning for Grasping Objects with an Anthropomorphic Hand

Grasping objects with high dimensional controllers such as an anthropomorphic hand using reinforcement learning is a challenging problem. In this work we experiment with a 16-D simulated version of a prosthetic hand developed for SouthHampton Hand Assessment Procedure (SHAP). We demonstrate that it is possible to learn successful grasp policies for an anthropomorphic hand from scratch using dee...