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SUBTA: A Framework for Supported User-Guided Bimanual Teleoperation in Structured Assembly

Xiao Liu, Prakash Baskaran, Songpo Li, Simon Manschitz, Wei Ma, Dirk Ruiken, Soshi Iba

TL;DR

SUBTA is presented, a supported teleoperation system for bimanual assembly that couples learned intention estimation, scene-graph task planning, and context-dependent motion assists that greatly improves both effectiveness and user experience in teleoperation.

Abstract

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.

SUBTA: A Framework for Supported User-Guided Bimanual Teleoperation in Structured Assembly

TL;DR

SUBTA is presented, a supported teleoperation system for bimanual assembly that couples learned intention estimation, scene-graph task planning, and context-dependent motion assists that greatly improves both effectiveness and user experience in teleoperation.

Abstract

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.
Paper Structure (20 sections, 4 equations, 7 figures, 6 tables)

This paper contains 20 sections, 4 equations, 7 figures, 6 tables.

Table of Contents

  1. INTRODUCTION
  2. Related Work
  3. Task-specific Teleoperation Framework
  4. Task and Intention Estimation
  5. Task Planning
  6. Motion Support Behaviors
  7. Experiments Design
  8. Experimental setup
  9. Task Estimation Evaluation
  10. Baselines
  11. Evaluation
  12. Subject Study
  13. The goal of the study
  14. Experimental Hypothesis
  15. Participant Study
  16. ...and 5 more sections

Figures (7)

  • Figure 1: SUBTA: The proposed Supported Teleoperation framework assists users in assembly tasks by (i) estimating the task and generating a plan, and (ii) providing real-time support through visual feedback and motion-level corrections.
  • Figure 2: Supported teleoperation system: The user teleoperates the robot to assemble the "horse" structure. The top row shows the scene graph encoding spatial relationships for task monitoring and planning. The middle row depicts the robot successfully executing the block assembly. The bottom row presents the digital twin environment, where task estimation and planning ((highlighted in red) are visualized to guide the user.
  • Figure 3: Task Planner: the planner computes the most likely target pose for the next block based on the graph edit distance (GED) and adds the corresponding node to the graph.
  • Figure 4: Eight Block assembly tasks.
  • Figure 5: Examples of participants performing three different assembly tasks using the SUBTA system during the user study. The supported workspace is projected on the left with the user included, while the actual robot workspace is shown on the right.
  • ...and 2 more figures