Reference number : 172-2023-2024-Q4-1016248, amendment number : , agreement type : G, recipient legal name : The Governors of the University of Alberta, recipient country : CA, recipient province : AB, recipient city : Edmonton|Edmonton, recipient postal code : T5J 4P6, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Model-Based Reinforcement Learning Robots for Transparent Vessel Identification and Manipulation in Self-Driving Labs, agreement title french : Robots d’apprentissage par renforcement basés sur des modèles pour l’identification et la manipulation de récipients transparents dans les laboratoires autonomes, agreement number : 1016248, agreement value : 247500.0, agreement start date : 2024-03-21, agreement end date : 2026-03-31, description english : Automation in chemistry labs expedites experimentation and minimizes errors, facilitating more cost-effective material and drug discovery. The project aims to automate chemistry labs by developing transferable, adaptable, and generalizable robotic systems using Reinforcement Learning (RL). The project is designed to tackle the challenges of integrating robotic arms into chemistry labs, focusing on minimizing data requirements and enhancing real-world applications of RL. Key objectives include developing a model-based RL method that generalizes across different robotic platforms, creating robust and transferable RL agents, and ensuring the real-time adaptability of robots to evolving lab tasks. The project addresses the unique challenge of manipulating transparent objects and aims to implement high frequency control for agile and responsive robot behavior. By extending the Remote-Local Distributed (ReLoD) system to model-based RL, the project aims to refine the real-time learning and control of lab robots, setting the stage for widespread adoption of automated, efficient, and intelligent lab systems., description french : Robots d’apprentissage par renforcement basés sur des modèles pour l’identification et la manipulation de récipients transparents dans les laboratoires autonomes, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada