AIARA is a cross-continent project that involves multiple partners from both Canada and Germany, including UBC and Kinova company from Canada, and DLR, ZAL, Fraunhofer, and Broetje from Germany. It receives funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Consortium for Research and Innovation in Aerospace in Québec (CRIAQ), and the German Aeronautical Research Program LuFo (LuFo-8). The AIARA project seeks novel solutions for flexible and versatile robotic systems that can automatically adapt themselves to the changing conditions of the environment. Instead of the conventional approaches where a specific program is developed for a fixed robotic task in an unchanged environment, this project is intended to develop artificial-intelligence-enabled concepts and methods to enhance the adaptability of the robotic systems in variable environments and their extendability to new robotic tasks. Machine learning approaches, including but not limited to reinforcement learning, supervised learning, unsupervised learning, transfer learning, and meta-learning, will be used to construct adaptive models and develop adaptive methods for robotic manipulation tasks in variable environments. Hardware and software test benches will be developed to evaluate the feasibility and applicability of the approaches. Our ambition is to provide a novel direction toward reliable automation of the manufacturing and aerospace industry using highly adaptive robots enabled by cutting-edge artificial intelligence technologies.

Period: 2021.03 - 2022.09

Role: Project leader as a postdoc

Principal Investigator: Prof. Dr. Homayoun Najjaran

Affiliation: University of British Columbia (UBC), University of Victoria (Uvic)

Funding Source: Natural Sciences and Engineering Research Council of Canada (NSERC), Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ)

Website: https://www.criaq.aero/en/projects/

Consortium: Kinova Company, German Aerospace Center (DLR), Fraunhofer-Gesellschaft, Center of Applied Aeronautical Research (ZAL), August Brötje GmbH

The main objectives of AIARA include:

• Develop software and hardware platform for robot manipulation tasks.

• Literature review and survey on adaptive robotic systems and artificial intelligent approaches.

• Propose novel concepts for adaptive robotic systems.

• Develop advanced artificial intelligent methods for adaptive robotic systems.

• Develop open-access libraries, programs, and datasets for adaptive robotic studies.

Publications

• Z. Zhang, J. Hong, A. M. S. Enayati, H. Najjaran*, "Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning". (Submitted) [arXiv]

• A. M. S. Enayati, Z. Zhang, K. Gupta, and H. Najjaran*, "Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation". (Submitted) [arXiv]

• J. Hong, Z. Zhang, A. M. S. Enayati, and H. Najjaran*, "Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives". (Submitted) [arXiv]

• R. Dershan, A. M. S. Enayati, Z. Zhang, D. Richert, and H. Najjaran*, "Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation", in IEEE Transactions on Artificial Intelligence, Early Access, doi: 10.1109/TAI.2023.3299252. [IEEEXplore][arXiv]

• A. M. Soufi Enayati, Z. Zhang, and H. Najjaran*,  "A methodical interpretation of adaptive robotics: Study and reformulation", in Neurocomputing, Vol. 512, no. 2022, pp. 381-397, 2022,  doi: 10.1016/j.neucom.2022.09.114. [ScienceDirect][ResearchGate]

• Z. Zhang, D. Wollherr, and H. Najjaran*,  "Disturbance estimation for robotic systems using continuous integral sliding mode observer", in International Journal of Robust and Nonlinear Control, Vol. 32, no. 14, pp. 7946-7966, Sept. 2022, doi: 10.1002/rnc.6252. [Wiley]

• Z. Zhang, R. Dershan, A. M. S. Enayati, M. Yaghoubi, D. Richert, and H. Najjaran*, "A High-Fidelity Simulation Platform For Industrial Manufacturing by Incorporating Robotic Dynamics Into an Industrial Simulation Tool", in IEEE Robotics and Automation Letters, Vol. 7, no. 4, pp. 9123-9128, 2022, doi: 10.1109/LRA.2022.3190096. [IEEEXplore]