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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 …
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 …
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
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 …
Markov Decision Process from data (state, action, next state tuples). The transition …
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach
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 …
complexity and uncertainty. In this paper, we present a framework proposing the use of the …