Advancing Robot Interaction Safety: A Teleoperated Shared-Control Approach Using a Lightweight Force-Feedback Exoskeleton
Bibliographic record
Abstract
Tele-homecare has become a promising approach to meet the growing demand for elderly and disability care. In such a context, ensuring human-robot interaction safety during teleoperation poses a critical challenge. Existing teleoperation control approaches focus solely on the robot’s end-effector trajectory, failing to handle inevitable or even desirable contacts on other robot links. This paper proposes a teleoperated shared-control strategy to deal with this challenge. A lightweight exoskeleton is developed to teleoperate the robot and give force feedback to the operator. Additionally, an exoskeleton-based shared-control strategy is proposed to integrate operator commands with real-time proximity sensing information, allowing the robot to avoid collisions while executing tasks. To react to inevitable contact, the force feedback function is incorporated into the proposed strategy to enable the operator to experience intuitive contact. Comparative experiments and a demonstration are designed to evaluate the feasibility and reliability of the proposed strategy in a tele-homecare scenario. Compared to the traditional teleoperation strategy, the proposed method can greatly reduce the contact forces on the robot’s links, indicating the potential of the proposed strategy in advancing safety in tele-homecare systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".