Call for Contributions

Call for Contributions

Challenge submission:

We are interested in collecting real-world lab challenges from domain experts such as chemists, biologists, or other natural scientists. Such lab challenges include but are not limited to labor-intensive or time-consuming tasks typically performed in labs that can not yet be done by existing automation systems, but that could potentially be addressed by a general-purpose robot manipulator and/or AI.

If you happen to have such a lab challenge in your daily work, please define it using our template: https://docs.google.com/document/d/18cwG7T_F0No-mIIEZYkH5u7KMrwdlqLTcV-XAIoFqs8/edit?tab=t.0

Submission is being done via Google Forms: https://docs.google.com/forms/d/e/1FAIpQLScNl900JbKt3kziM4GTEgThD2yZ3ylBTRnDKxsUn6tXq1bGZg/viewform

Review process:
All submitted challenges will be peer-reviewed in a single-blind process. 

Publication:

Selected challenges will be presented in spotlight presentations during the workshop. After the spotlights, there will be an interdisciplinary discussion with natural scientists, engineers, and AI experts on the challenges and potential solutions.
The submitted PDFs will be published on the workshop website and may serve in the future as benchmark challenges.

Topics of Interest

This workshop covers, but is not limited to, the following topics of interest:

• Robotics, AI and Automation in Laboratories: Past, Present and Future

• Application of Robotics, AI, and Automation to Life Sciences, Materials Research, Clinical Laboratories, and Pharmaceutical Development

• Mobile Manipulation for Laboratory Automation

• Generic Robotic Skill Development of Laboratory Tasks

• Human-Robot Collaboration/Interaction in Laboratories

• Cognitive Robotics and Artificial Intelligence for Laboratory Environments

• Safety in Robotic+AI Lab Automation

• Knowledge Discovery and Transfer of Experimental Results: From Hypothesis to Fundamental Understanding

• Challenges in Lab Automation

• Standardization in Laboratory Automation

• System Architectures for Laboratory Automation

• Self-Driving Labs

• Case Studies in Implementing Laboratory Automation

• Integration of Robotic Systems and Laboratory Instruments

• Agentic AI for Lab Automation

• Foundation Models and Generative AI for General-purpose Robots in Labs

• Digital Twins and Simulators for  Laboratory Environments

• Machine Vision Methods for Laboratory Robotics

• Multimodal Sensing and Perception for Robots in Labs

• Digital Twins and Simulators for  Laboratory Environments