An Admittance-controlled Force-scaling Dexterous Assistive Robotic System
Bibliographic record
Abstract
Play has a vital role in a child’s development; it can affect everything from social and language to cognitive and perceptual skills. However, if a child has a physical disability, the fundamental limitations of their disability may prevent them from participating in all forms of play. Construction and block play is an example of play that may be difficult for children who have reduced upper body strength and are, therefore, unable to manipulate heavier objects in space. In this paper, we propose a novel 6 degree-of-freedom admittance-controlled, force-scaling robot that will allow for children to lift heavier objects than they would normally be able to, while still retaining the full range of motion of their upper body. This assistive system is designed to retain the user’s haptic perception, allowing the user to still partially feel the weight of the objects that they are manipulating. Two user studies are done to evaluate the usability of the system. First, to ensure that the force scaling of the system does not negatively affect a user’s haptic perception, 10 able-bodied individuals were asked to order a series of buckets with identical appearances but different masses from lightest to heaviest with three different force-scaling factors. It was shown that the force amplification ability of the system does not significantly detract from users’ ability to discriminate masses. Second, to evaluate the precision and the usefulness of the force scaling of the system, users were asked to perform a challenging peg-in-hole insertion task. Results indicate that the system has a positive effect on the ability of a user to perform the task when the assistance is necessary. However, increasing amounts of assistance, past those required for participants to complete the task without issues, do not have any significant effect. The effect of a modular reacher bar that can augment the workspace of users is investigated through a similar peg-in-hole insertion task. For the trials with the modular reacher bar attached, it is shown that the system’s force amplification has a very positive effect in assisting users in completing the task. It should be noted that although the target population for this paper is children with disabilities, there can also be uses for this system as a general assistive technology for adults with upper-body weakness in their daily lives.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".