Oct 11, 2022 · To enable in-hand manipulation, existing deep reinforcement learning based approaches mainly focus on training a single robot-structure-specific ...

Jun 2, 2023 · To enable in-hand manipulation, existing deep reinforcement learning-based approaches mainly focus on training a single robot-structure-specific ...

Jun 6, 2024 · PDF | On May 29, 2023, Lingfeng Tao and others published A Multi-Agent Approach for Adaptive Finger Cooperation in Learning-based In-Hand ...

In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and the complex interaction with the object.

May 29, 2023 · This perspective documents the ongoing attempts to undermine the core principles of liberal epistemology and to replace merit with non- ...

A multi-agent approach for adaptive finger cooperation in learning-based in-hand manipulation. L Tao, J Zhang, M Bowman, X Zhang. 2023 IEEE International ...

Aug 29, 2024 · A Multi-Agent Approach for Adaptive Finger Cooperation in Learning-based In-Hand Manipulation. ICRA 2023: 3897-3903. [i10]. view. electronic ...

This work proposes the Finger-specific Multi-agent Shadow Reward (FMSR) method to determine the stable manipulation constraints in the form of dense reward ...

Three research objectives are developed to address these issues: 1) developing a multi-agent approach that models the in-hand manipulation as a cooperation task ...

This award supports research to develop and test a control framework for stable in-hand telemanipulation, using a physic-informed machine learning approach that ...