Data-driven design of expressive robot hands and hand gestures : applications for collaborative human-robot interaction
Bibliographic record
Abstract
Fast and reliable communication between human workers and robotic assistants (RAs) is essential for successful collaboration between these agents. This is especially true for typically noisy manufacturing environments that render verbal communication less effective. This thesis investigates the efficacy of nonverbal communication capabilities of robotic manipulators that have poseable, three-fingered end-effectors (hands). This work explores 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, a series of three studies were conducted. Study 1 empirically explored the type of hand configurations that humans use to nonverbally instruct another per- son (N=17). Based on the findings from Study 1, Study 2 examined how well human gestures with frequently used hand configurations were under- stood by recipients of the message (N=140). Finally, Study 3 implemented the most human-recognized human hand configurations on a 7-degree-of- freedom (DOF) robotic manipulator to investigate the efficacy of having human-inspired hand poses on a robotic hand compared to an unposed hand (N=100). Contributions of this work include the 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 expressed using different hand configurations. These experimental results indicate that most gestures are better recognized with a higher level of confidence when displayed with a posed robot hand. Guidelines and principles are provided based on these results for the mechanical design of robotic hands.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".