Review of deep reinforcement learning-based object gras**: Techniques, open challenges, and recommendations

MQ Mohammed, KL Chung, CS Chyi - Ieee Access, 2020 - ieeexplore.ieee.org
The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

Enabling Human-Robot Partnerships in Digitally-Driven Construction Work through Integration of Building Information Models, Interactive Virtual Reality, and Process …

X Wang - 2022 - deepblue.lib.umich.edu
Human cognition plays a critical role in construction work, particularly in the context of high-
level task planning and in-field improvisation. On the other hand, robots are adept at …

Learning parametric policies and transition probability models of markov decision processes from data

T Xu, H Zhu, IC Paschalidis - European journal of control, 2021 - Elsevier
We consider the problem of estimating the policy and transition probability model of a
Markov Decision Process from data (state, action, next state tuples). The transition …

Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach

R Giahi, CA MacKenzie, R Bijari - arxiv preprint arxiv:2312.17284, 2023 - arxiv.org
Engineering system design, viewed as a decision-making process, faces challenges due to
complexity and uncertainty. In this paper, we present a framework proposing the use of the …