Systematic and Robust Control Framework for Immersive Bilateral Teleoperation : Toward Human Skill Transfer to Beyond-Human Scale

Abstract

Heavy-duty hydraulic manipulators (HHMs) are in high demand across industries such as forestry, mining, agriculture, and marine operations due to their exceptional robustness and superior payload-to-weight ratio compared to electric counterparts, which are often constrained by structural rigidity. Thus, the automation of HHMs is of significant industrial interest. However, despite these advantages, HHMs face fundamental challenges that impede their automation, including unknown uncertainties and inherent complexities arising from their governing fluid dynamics, posing major obstacles that significantly slow down their automation process and ultimately limit their integration into modern intelligent robotic systems.

This dissertation proposes a systematic control framework that not only addresses these challenges but also accelerates the automation process by enabling human skill transfer to HHMs, robotic manipulators with beyond-human-scale design. To this end, four interrelated research questions (RQs) have been identified, which collectively fall under three main lines of investigation. The first line examines how to design a control architecture for HHMs that ensures accuracy and robustness during free motion and impact resilience in contact-rich tasks, while remaining computationally efficient for real-time deployment. The second addresses how to ensure the safety of the human operator in the loop by systematically accounting for compound input nonlinearities, unknown dynamics, and other unmodeled effects, while promoting human adaptivity without imposing any gender-specific constraints on the controller. The third concerns the development of a stable and transparent force-reflected bilateral teleoperation framework that fosters a high level of operator immersion and sense of embodiment, enabling effective human engagement in tele-operation of beyond-human-scale robotic manipulators under extreme motion and force scaling.

This dissertation addresses the research questions through a coherent body of work comprising five peer-reviewed publications and one unpublished manuscript. Collectively, these works advance the state of the art in robust, immersive, and human-adaptive bilateral teleoperation with guaranteed transparency and stability. For the first time, the proposed solution provides a reliable framework for effective data collection and skill transfer to beyond-human-scale robotic manipulators. The proposed methods have been extensively validated through real-world experiments, confirming the practical feasibility and robustness of the framework. In addition, an initial user study was conducted to assess the significance of the findings, demonstrating the effectiveness of the methodology in fulfilling the thesis objectives.