An Adaptive Robotics Framework for Chemistry Lab Automation

Yoshikawa, N.; Li, Andrew Zou; Darvish, Kourosh; Zhao, Yuchi; Xu, Haoping; Aspuru‐Guzik, Alán; Garg, Animesh; Shkurti, Florian

doi:10.48550/arxiv.2212.09672

Cited by 5 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The liquid dispensing accuracy between our proposed pipette and pouring by a robot [3] was compared. The result is shown in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…The robot is equipped with an additional servo motor to increase the dexterity of the robot. Details of the robot are described in [3]. The procedure of the experiment is as follows: (i) the robot grasps the pipette, (ii) the robot moves the pipette into a beaker filled with water and inhales water, (iii) the robot moves the pipette above a Petri dish and dispenses the liquid inside the pipette.…”

Section: Methodsmentioning

confidence: 99%

“…The rotational velocity of the end-effector was controlled by the error from the target weight and shaping function that compensates for the feedback delay from the scale. The details of the pouring skill are described in [3]. We measured the pouring performance in 10 and 20 mL.…”

Section: Methodsmentioning

confidence: 99%

“…One of the common actions that robots require to perform successfully in chemistry, materials, science and biology self-driving laboratories is that of transferring liquids accurately. Liquid pouring for chemistry experiments using a robot arm and regular glassware was proposed by Yoshikawa et al [3], but its accuracy is limited due to the delayed feedback from the sensors. This work seeks to overcome those limitations by providing a simple solution that can be extended to many robotic settings.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a 3D-printed digital pipette designed for robot gripper. The proposed pipette transfer liquids in our robot chemist system [3] as shown in Figure 1. Our pipette uses a low-cost commercial syringe to hold liquids and a linear actuator to handle the plunger of the syringe.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Digital pipette: Open hardware for liquid transfer in self-driving laboratories

Yoshikawa

Darvish

Garg

et al. 2023

Preprint

View full text Add to dashboard Cite

Self-driving laboratories promise to democratize automated chemical laboratories. Accurate liquid handling is an essential operation in the context of chemical labs, and consequently a self-driving laboratory will require a robotic liquid handling and transfer. Although many pipettes are available for human scientists, robots cannot manipulate these pipettes due to the limitations of current robot gripper morphology. We propose an intuitive yet elegant design for a 3D-printed digital pipette designed for robots to carry out chemical experiments. Performance-evaluation experiments were carried out liquid transfer tasks. Our results show that robots with digital pipette could transfer liquids within 0.5% error. This error is comparable to the baseline set by commercially available human-handled pipettes.

show abstract

“…The liquid dispensing accuracy between our proposed pipette and pouring by a robot [3] was compared. The result is shown in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Digital pipette: Open hardware for liquid transfer in self-driving laboratories

Yoshikawa

Darvish

Garg

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

The Impact of Digitalized Data Management on Materials Systems Workflows

Tristram,

Jung,

Hodapp

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

The basic modules for materials research are systems for the design, synthesis, preparation, analysis, and application of materials and materials systems. To be efficient and produce findable, accessible, interoperable, and reusable (FAIR) data, state‐of‐the‐art materials research needs to consider the integration of research data management (RDM) workflows and, in the end, the implementation of process automation concepts for all parts of the main modules. Here, the state‐of‐the‐art methods of RDM in academia are described and a perspective on the future of digitalized molecular material systems workflows is given. The different elements of an integrated research data management strategy are described, and examples of automated processes are depicted. As such, the use of electronic lab notebooks for comprehensive documentation, the use of data‐integration and data‐conversion strategies, and the establishment of two platforms that enable the automated synthesis of chemical components for materials and the analysis of materials by electron microscopy, are highlighted. Two examples of beneficial effects of successful RDM strategies are presented, showing a sophisticated tool for data prediction based on machine learning and options for creating community‐driven databases by extracting and re‐using data from different scientific projects.

show abstract

Large language models for chemistry robotics

Yoshikawa,

Skreta,

Darvish

et al. 2023

Auton Robot

Self Cite

View full text Add to dashboard Cite

This paper proposes an approach to automate chemistry experiments using robots by translating natural language instructions into robot-executable plans, using large language models together with task and motion planning. Adding natural language interfaces to autonomous chemistry experiment systems lowers the barrier to using complicated robotics systems and increases utility for non-expert users, but translating natural language experiment descriptions from users into low-level robotics languages is nontrivial. Furthermore, while recent advances have used large language models to generate task plans, reliably executing those plans in the real world by an embodied agent remains challenging. To enable autonomous chemistry experiments and alleviate the workload of chemists, robots must interpret natural language commands, perceive the workspace, autonomously plan multi-step actions and motions, consider safety precautions, and interact with various laboratory equipment. Our approach, CLAIRify, combines automatic iterative prompting with program verification to ensure syntactically valid programs in a data-scarce domain-specific language that incorporates environmental constraints. The generated plan is executed through solving a constrained task and motion planning problem using PDDLStream solvers to prevent spillages of liquids as well as collisions in chemistry labs. We demonstrate the effectiveness of our approach in planning chemistry experiments, with plans successfully executed on a real robot using a repertoire of robot skills and lab tools. Specifically, we showcase the utility of our framework in pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization. Further details about CLAIRify can be found at https://ac-rad.github.io/clairify/.

show abstract

An Adaptive Robotics Framework for Chemistry Lab Automation

Cited by 5 publications

References 0 publications

Digital pipette: Open hardware for liquid transfer in self-driving laboratories

Digital pipette: Open hardware for liquid transfer in self-driving laboratories

The Impact of Digitalized Data Management on Materials Systems Workflows

Large language models for chemistry robotics

Contact Info

Product

Resources

About