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Learning Behavior Trees for Collaborative Robotics

Time: Mon 2023-06-12 10.00

Location: Kollegiesalen, Brinellvägen 8, Stockholm

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Language: English

Subject area: Computer Science

Doctoral student: Matteo Iovino , Robotik, perception och lärande, RPL

Opponent: Karinne Ramírez-Amaro, Chalmers Tekniska Högskola, Göteborg, Sverige

Supervisor: Christian Smith, Robotik, perception och lärande, RPL

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QC 20230523


This thesis aims to address the challenge of generating task plans for robots in industry-relevant scenarios. With the increase in small-batch production, companies require robots to be reprogrammed frequently for new tasks. However, maintaining a team of operators with specific programming skills is only cost-efficient for large-scale production. The increase in automation targets companies where humans share their working environment with robots, expanding the scope of manufacturing applications. To achieve that, robots need to be controlled by task plans, which sequence and optimize the execution of actions. This thesis focuses on generating task plans that are reactive, transparent and explainable, modular, and automatically synthesized. These task plans improve the robot’s autonomy, fault-tolerance, and robustness. Furthermore, such task plans facilitate the collaboration with humans, enabling intuitive representations of the plan and the possibility for humans to prompt instructions at run-time to modify the robot’s behavior. Lastly, autonomous generation decreases the programming skills required for the operator to program a robot, and optimizes the task plan. This thesis discusses the use of Behavior Trees (BTs) as policy representations for robotic task plans. It compares the modularity of BTs and Finite State Machines (FSMs) and concludes that BTs are more effective for industrial scenarios. This thesis also explores the automatic and intuitive generation of BTs using Genetic Programming and Learning from Demonstration methods, respectively. The proposed methods aim to time-efficiently evolve BTs for mobile manipulation tasks and allow non-expert users to intuitively teach robots manipulation tasks. This thesis highlights the importance of user experience in task solving and how it can benefit evolutionary algorithms. Finally, it proposes the use of previously learned BTs from demonstration to intervene in the unsupervised learning process.