Toward a Natural Language Interface for Transferring Grasping Skills to Robots
Bibliographic record
Abstract
In this paper, we report on the findings of a human-robot interaction study that aims at developing a communication language for transferring grasping skills from a nontechnical user to a robot. Participants with different backgrounds and education levels were asked to command a five-degree-of-freedom human-scale robot arm to grasp five small everyday objects. They were allowed to use either commands from an existing command set or develop their own equivalent natural language instructions. The study revealed several important findings. First, individual participants were more inclined to use simple, familiar commands than more powerful ones. In most cases, once a set of instructions was found to accomplish the grasping task, few participants deviated from that set. In addition, we also found that the participant's background does appear to play a role during the interaction process. Overall, participants with less technical backgrounds require more time and more commands on average to complete a grasping task as compared to participants with more technical backgrounds.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".