Performing a large volume of experiments in Chemistry labs creates repetitive actions costing researchers time, automating these routines is highly desirable. Previous experiments in robotic chemistry have performed high numbers of experiments autonomously, however, these processes rely on automated machines in all stages from solid or liquid addition to analysis of the final product. In these systems every transition between machine requires the robotic chemist to pick and place glass vials, however, this is currently performed using open loop methods which require all equipment being used by the robot to be in well defined known locations. We seek to begin closing the loop in this vial handling process in a way which also fosters human-robot collaboration in the chemistry lab environment. To do this the robot must be able to detect valid placement positions for the vials it is collecting, and reliably insert them into the detected locations. We create a single modality visual method for estimating placement locations to provide a baseline before introducing two additional methods of feedback (force and tactile feedback). Our visual method uses a combination of classic computer vision methods and a CNN discriminator to detect possible insertion points, then a vial is grasped and positioned above an insertion point and the multi-modal methods guide the final insertion movements using an efficient search pattern. Through our experiments we show the baseline insertion rate of 48.78% improves to 89.55% with the addition of our "force and vision" multi-modal feedback method.

翻译：在化学实验室中执行大量实验会产生重复性操作，消耗研究人员大量时间，因此实现这些常规操作的自动化具有重要价值。以往的化学机器人实验已实现自主执行大量实验，但这些流程从固体/液体添加环节到最终产物分析均依赖自动化设备。在此类系统中，设备间的每次转换都需要化学机器人拾取和放置玻璃样品瓶，目前这一过程采用开环方法实现，要求机器人使用的所有设备均处于精确已知的固定位置。我们致力于在样品瓶处理流程中建立闭环控制，同时促进化学实验室环境下的人机协作。为此，机器人需能检测待采集样品瓶的有效放置位置，并可靠地将样品瓶插入检测到的位置。我们首先建立基于单一视觉模态的位置估计方法作为基线，随后引入力觉与触觉两种反馈手段。视觉方法结合经典计算机视觉技术与CNN判别器检测潜在插入点，机械臂抓取样品瓶并将其定位至插入点上方后，多模态方法通过高效搜索模式引导最终插入动作。实验证明，基线方法的插入成功率为48.78%，通过引入"力觉与视觉"多模态反馈方法，该指标提升至89.55%。