Bibliographic record
Abstract
This paper presents an integrated system that combines learning, a natural-language interface, and robotic grasping to enable the transfer of grasping skills from nontechnical users to robots. The system consists of two parts: a natural-language interface for grasping commands and a learning system. This paper focuses on the learning system and testing of the entire system in a small usability study. The learning system presented consists of two phases. In the first phase, the system learns to predict the next command, which the user is planning to issue based on command sequences recorded during previous grasping sessions. In the second phase, the system predicts the user's current state and moves the robot's gripper to the intended target endpoint to attempt to grasp the object. Using eight nontechnical users and a 5-degree-of-freedom (DOF) robot arm, a usability study was conducted to observe the impact of the learning system on user performance and satisfaction during a grasping operation. Experimental results show that the system was effective in learning users' grasping intentions, which allowed it to reduce the average time to grasp an object. In addition, participants' feedback from the usability study was generally positive toward having an adaptive robotics system that learns from their commands.
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 | 0.000 | 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) | 0.000 | 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".