LucidGrasp: Robotic Framework for Autonomous Manipulation of Laboratory Equipment with Different Degrees of Transparency via 6D Pose Estimation

TL;DR

LucidGrasp introduces a robotic framework capable of autonomously manipulating transparent laboratory objects with high precision using 6D pose estimation, enhancing automation in complex laboratory tasks.

Contribution

The paper presents a novel robotic system that accurately estimates poses of transparent objects and performs complex manipulation tasks in laboratory automation.

Findings

Robust visual perception system for transparent objects

Accurate 6D pose estimation in complex scenarios

Successful autonomous liquid dispensing operations

Abstract

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability.