Learning robotic manipulation skills with multiple semantic goals by conservative curiosity-motivated exploration

Learning robotic manipulation skills with multiple semantic goals by conservative curiosity-motivated exploration

Reinforcement learning (RL) empowers the agent to learn robotic manipulation skills autonomously. Compared with traditional single-goal RL, semantic-goal-conditioned RL expands the agent capacity to accomplish multiple semantic manipulation instructions. However, due to sparsely distributed semantic...

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Bibliographic Details
Main Authors: Changlin Han, Zhiyong Peng, Yadong Liu, Jingsheng Tang, Yang Yu, Zongtan Zhou
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-03-01
Series:Frontiers in Neurorobotics
Subjects:
hybrid policy mechanism
sparse reward
semantic goal
reinforcement learning
deep neural networks
Online Access:https://www.frontiersin.org/articles/10.3389/fnbot.2023.1089270/full

Internet

https://www.frontiersin.org/articles/10.3389/fnbot.2023.1089270/full

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