Automatic behavior tree generation for enhanced human–robot collaborative task planning in industry 5.0: A systematic review

Automatic behavior tree generation for enhanced human–robot collaborative task planning in industry 5.0: A systematic review

22/06/2026

Authors: Pierre Hémono, Ahmed Nait Chabane, M’hammed Sahnoun, Martin Choux

“In the context of Industry 5.0, scheduling heterogeneous resources, such as humans and robots, has become increasingly critical. Task allocation must balance human comfort, ergonomics, and trust with productivity and responsiveness to customer demands. This review explores recent advances and prospects in the automatic generation of schedules and action plans, particularly Behavior Trees (BTs), to improve human–robot collaboration. We examine the application of artificial intelligence techniques to classical production management problems, such as Job Shop Scheduling Problem (JSSP) and Assembly Line Balancing Problems (ALBP), for autonomous task scheduling and robotic behavior design. This includes highlighting innovative scheduling approaches and the advantages of Behavior Trees over traditional models such as Hierarchical Task Networks (HTN) and Finite-State Machines (FSM). Behavior Trees offer a modular and reactive programming structure essential for executing complex tasks assigned to robots. The review also discusses human operators’ perception of robotic actions and identifies best practices for implementing collaborative solutions that prioritize both efficiency and safety.”

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