Teaching Robot End Effectors to Grasp Construction Tools Based on Deep Reinforcement Learning

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Success in training robots to grasp construction tools indicates advancements in automation technology.
The policy learning framework demonstrated effectiveness in manipulating irregular objects with a reasonable success rate.
Using the Critic-Actor algorithm, the research showcases a novel approach to enhancing motor skills in robotic applications.
This method may significantly reduce labor costs and enhance efficiency in various construction tasks.

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Simulation-based reinforcement learning improves grasping abilities in robotic arms, indicating efficiency gains in automated construction tasks.

Yu et al. (2025) studied this question.

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Simulation-based reinforcement learning improves grasping abilities in robotic arms, indicating efficiency gains in automated construction tasks.