Development and Evaluation of a Deep Q-Network-Based Robot Learning Paradigm in Real-World Human-Robot Collaborative Tasks

As robot systems continue to be advanced and implemented across industries, they do so typically by two methodologies, including standalone systems and collaborative ones. Standalone systems are typically set in their own areas, away from human workers. Collaborative robots share a common workspace with their human counterparts and work with them to complete tasks together efficiently and safely. Within this category of robotics, there exists another subcategory that describes the method of implementation and usage rather than simply the type of system. This subcategory involves how the machine will interact with workers and understand its role in interaction. Thus, it raises interest in the field of learning from demonstrations, where the robot may dynamically learn the behavior that is desired by the user rather than being explicitly hardcoded to perform its task. In this work, we develop a deep Q-network-based robot learning paradigm for human-robot partnerships in shared tasks. The proposed approach is validated in real-world human-robot collaborative contexts. In addition, to assess the performance of this approach and the acceptance from practitioners, we conduct a multi-metric user study. Implementation and evaluation results indicate that the developed solution works effectively for human-robot teamwork and receives high support from active users who rate it very well on several key metrics. The future work of this study is also discussed.