Vision-Based Contact Wrench Estimation in Human-Robot Interaction
Bibliographic record
Abstract
With the rapid integration of robotics across diverse sectors, human interaction with these technologies is becoming inevitable. Ensuring safety is increasingly crucial to prevent injuries and maintain effective interactions. Accurate force estimation enables robots to sense contact forces and respond appropriately. This paper presents a vision-based estimation method for multi-contact physical human-robot interaction. Utilizing an RGB-D sensor, it detects 3D hand positions to identify contact points and employs a generalized momentum observer to distinguish joint torques from external wrenches. A long short-term memory network compensates for uncertainties arising from unmodelled dynamics. Addressing challenges like wrench null space and Jacobian singularities, the approach identifies computable external wrench components. The method achieves a 0.9 N estimation error in complex, multi-contact interactions, enhancing safety and responsiveness. Key contributions include a novel wrench identification method leveraging robot configuration and contact points, derived from a vision-based system, to enhance real-time estimation.
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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) | 0.001 | 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".