An algorithm for drug retrieval based on robot-grasping detection constraints and DDPG autonomous learning
Xiaowen Zhang, Tiegang Lv, Andrea Tigrini, Andrea Tigrini, Andrea Tigrini · 2025
Read An algorithm for drug retrieval based on robot-grasping detection constraints and DDPG autonomous learning by Xiaowen Zhang, Tiegang Lv, Andrea Tigrini, Andrea Tigrini, Andrea Tigrini (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12747349.txt.
Description
Xiaowen Zhang and Tiegang Lv present a learning method for pharmacy robots in this 2025 paper. A medicine-picking robot must grasp each drug pack firmly and precisely. Its success depends on visual guidance that finds the right grasp point. In messy, unstructured settings that search can be slow and error prone.
Their method works in two steps, guided by detection constraints. First, it marks candidate grasp areas from the outline of each medicine pack. Then a deep reinforcement learning system, DDPG, learns the best grasp point from those marked images. It explores with added noise and feeds results back in real time. Tests showed a clear gain in grasp detection accuracy over prior methods.
Catalog record
| CREATOR | Xiaowen Zhang, Tiegang Lv, Andrea Tigrini, Andrea Tigrini, Andrea Tigrini |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 90 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |