Cooperative gestures for industry: Exploring the efficacy of robot hand configurations in expression of instructional gestures for human–robot interaction
Bibliographic record
Abstract
Fast and reliable communication between human worker(s) and robotic assistants is essential for successful collaboration between the agents. This is especially true for typically noisy manufacturing environments that render verbal communication less effective. In this work, we investigate the efficacy of nonverbal communication capabilities of robotic manipulators that have poseable, three-fingered end-effectors (hands). We explore the extent to which different poses of a typical robotic gripper can effectively communicate instructional messages during human–robot collaboration. Within the context of a collaborative car door assembly task, we conducted a series of three studies. We first observed the type of hand configurations that humans use to nonverbally instruct another person (Study 1, N = 17); based on the observation, we examined how well human gestures with frequently used hand configurations are understood by recipients of the message (Study 2, N = 140). Finally, we implemented the most human-recognized human hand configurations on a seven-degree-of-freedom robotic manipulator to investigate the efficacy of having human-inspired hand poses on a robotic hand compared to an unposed hand (Study 3, N = 100). Contributions of this work include presentation of a set of hand configurations humans commonly use to instruct another person in a collaborative assembly scenario, as well as recognition rate and recognition confidence measures for the gestures that humans and robots express using different hand configurations. Results indicate that most gestures are better recognized with a higher level of confidence when displayed with a posed robot hand.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".