SUBTA: A Framework for Supported User-Guided Bimanual Teleoperation in Structured Assembly
This work addresses the challenge of enhancing precision and user experience in teleoperation for structured assembly tasks, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of improving human-robot collaboration in bimanual assembly teleoperation by developing SUBTA, a system that integrates intention estimation, task planning, and motion assists, resulting in significant improvements in position accuracy (d=1.18), orientation accuracy (d=1.75), and reduced mental demand (d=1.34) compared to standard teleoperation.
In human-robot collaboration, shared autonomy enhances human performance through precise, intuitive support. Effective robotic assistance requires accurately inferring human intentions and understanding task structures to determine optimal support timing and methods. In this paper, we present SUBTA, a supported teleoperation system for bimanual assembly that couples learned intention estimation, scene-graph task planning, and context-dependent motion assists. We validate our approach through a user study (N=12) comparing standard teleoperation, motion-support only, and SUBTA. Linear mixed-effects analysis revealed that SUBTA significantly outperformed standard teleoperation in position accuracy (p<0.001, d=1.18) and orientation accuracy (p<0.001, d=1.75), while reducing mental demand (p=0.002, d=1.34). Post-experiment ratings indicate clearer, more trustworthy visual feedback and predictable interventions in SUBTA. The results demonstrate that SUBTA greatly improves both effectiveness and user experience in teleoperation.