Vision-guided adaptive scooping for powder weighing in autonomous chemistry laboratories

Abstract

Autonomous, high-accuracy powder dispensing of heterogeneous solid materials remains an open challenge in automated chemistry laboratories. Existing systems often fail with diverse powder morphologies because they neglect the critical initial material acquisition step (i.e., scooping). We present an end-to-end, vision-guided powder dispensing system that integrates an adaptive scooping mechanism with a deep reinforcement learning-based policy for dispensing. Our system utilises a parametrised scooping motion and assesses visually the acquired material volume after each scooping attempt. This visual feedback drives an iterative correction loop, allowing the robot to adjust its motion parameters to reject failures and obtain a suitable quantity for subsequent dispensing. We evaluated our system in real laboratory conditions using a set of 7 powders with varying physical properties. Our experiments demonstrate that the fully adaptive system outperformed fixed scooping baselines, achieving the lowest average absolute weighing error of 1.93 mg ± 2.04 mg across all materials.