Preference-Controllable Multi-Objective Deep Reinforcement Learning for Human-Robot Task Allocation in Service Environments

Human–Robot Collaboration (HRC) has gained increasing attention as it expands from industrial environments to service-oriented settings, where dynamic conditions and diverse operational objectives pose significant challenges for task allocation. Unlike controlled industrial environments, service contexts are characterized by frequent changes, uncertainty, and time-varying priorities, rendering static task allocation strategies ineffective. This paper proposes a method to address the problem of determining the optimal balance between human and robotic task allocation in dynamic service-oriented HRC systems. A preference-controllable multi-objective deep reinforcement learning framework is introduced to formulate task allocation as a dynamic, preference-dependent decision-making process. The proposed approach explicitly captures trade-offs among multiple, potentially conflicting objectives and enables adaptive task allocation under changing operational conditions and service priorities. The framework is evaluated through simulation-based experiments and comparative analysis with baseline strategies using multiple evaluation metrics, complemented by additional validation using external datasets. Experimental results demonstrate the effectiveness and adaptability of the proposed approach across varying preference configurations and workload conditions, supporting its applicability in real-world smart service environments.

@article{Alahmari2026,
  title     = {Preference-Controllable Multi-Objective Deep Reinforcement Learning for Human-Robot Task Allocation in Service Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Asmaa Rashed Alahmari and Wadee Alhalabi},
  doi       = {10.14569/IJACSA.2026.0170430},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170430}
}