Gaze to Grasp: Shared Autonomy in VR Robot Teleoperation <sup>*</sup>
Bibliographic record
Abstract
Shared autonomy in robot teleoperation can ease task completion and lower cognitive load for operators, by combining human intent with the autonomous capabilities of robots. As many manipulation control tasks involve the grasping of objects as the first step, augmenting assistance at this stage has the potential to improve user experience and task performance. This work discusses a new grasping assistance framework based on users’ intent signalling via eye gaze. Specifically, the eye gaze direction is retrieved from a virtual reality headset during the teleoperation. This information is used to automatically determine grasping locations on the target object, after which the grasping sequence is executed without the need to perform 1-to-1 motion mapping. The grasping assistance system is implemented using ROS2 to control a Kinova Gen 3 robotic manipulator, using a Meta Quest Pro virtual reality headset. A user study was performed with 30 participants using the developed system to compare the usability, workload, and performance of the grasping-assisted teleoperation with pure teleoperation (motion mapping). Results show that grasping assistance significantly reduces users’ workload, but also leads to lower performance metrics with respect to pure teleoperation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".