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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

An algorithm for drug retrieval based on robot-grasping detection constraints and DDPG autonomous learning.

Xiaowen Zhang¹, Tiegang Lv¹

¹Taiyuan Institute of Technology, Taiyuan, Shanxi, China.

|December 29, 2025

Summary

A new self-learning algorithm improves medicine-picking robot accuracy by analyzing drug boundaries and using deep reinforcement learning for precise grasping. This method enhances robot learning speed and success rates in pharmacies.

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Robot grasping accuracy is crucial for tasks like medicine retrieval.
Current methods struggle with precision in unstructured pharmacy environments.

Purpose of the Study:

To enhance medicine-picking robot learning speed and grasping accuracy.
To reduce the search space for grasping poses.
To improve performance in unstructured environments.

Main Methods:

Proposed a self-learning Deep Deterministic Policy Gradient (DDPG) grasping algorithm.
Extracted candidate grasping areas by analyzing medicine boundaries.
Utilized deep reinforcement learning for real-time optimal grasping point detection and pose adjustment.

Main Results:

Achieved a minimum 15% improvement in grab detection accuracy compared to other methods.
Demonstrated a 95% grab success rate within the confidence interval.
Verified the feasibility and effectiveness of the proposed algorithm.

Conclusions:

The self-learning DDPG algorithm significantly improves grasping accuracy for medicine-picking robots.
The method is effective for automated pharmacy applications.
This approach enhances robotic capabilities in complex environments.