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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".