Gordon Daniel F N, Christou Andreas, Stouraitis Theodoros, Gienger Michael, Vijayakumar Sethu
The University of Edinburgh, Edinburgh, UK.
The Alan Turing Institute, London, UK.
R Soc Open Sci. 2023 Jun 28;10(6):221617. doi: 10.1098/rsos.221617. eCollection 2023 Jun.
Robots and other assistive technologies have a huge potential to help society in domains ranging from factory work to healthcare. However, safe and effective control of robotic agents in these environments is complex, especially when it involves close interactions and multiple actors. We propose an effective framework for optimizing the behaviour of robots and complementary assistive technologies in systems comprising a mix of human and technological agents with numerous high-level goals. The framework uses a combination of detailed biomechanical modelling and weighted multi-objective optimization to allow for the fine tuning of robot behaviours depending on the specification of the task at hand. We illustrate our framework via two case studies across assisted living and rehabilitation scenarios, and conduct simulations and experiments of triadic collaboration in practice. Our results indicate a marked benefit to the triadic approach, showing the potential to improve outcome measures for human agents in robot-assisted tasks.
机器人和其他辅助技术在从工厂作业到医疗保健等诸多领域具有极大潜力来帮助社会。然而,在这些环境中对机器人代理进行安全有效的控制很复杂,尤其是当涉及密切互动和多个参与者时。我们提出了一个有效的框架,用于在包含人类和技术代理且有众多高层次目标的系统中优化机器人及互补辅助技术的行为。该框架结合了详细的生物力学建模和加权多目标优化,以便根据手头任务的规格对手机器人行为进行微调。我们通过辅助生活和康复场景中的两个案例研究来说明我们的框架,并在实践中进行三元协作的模拟和实验。我们的结果表明三元方法有显著益处,显示出在机器人辅助任务中改善人类代理结果指标的潜力。