Collaborative behavior-based approach for robot natural language interfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis describes a novel approach called the collaborative behavior-based approach. The approach is used to create an intelligent robot natural language interface so that ambiguous and uncertain human user instructions can be transformed into robot behavior-based control commands. Special features of a user-robot system have been taken into account. Knowledge about the robot world, predicted and history behaviors of the robot and the user are used to resolve ambiguity and uncertainty in interpreting the user instructions. The knowledge is stored in three knowledge bases namely world, history, and behavior. The world knowledge base stores information about the robot word's objects, relationships between the objects, and possible behaviors of the robot and the user. The behavior knowledge base stores information about a sequence of predicted behaviors of the robot and the user in completing services. The history knowledge base stores behaviors of the robot that already occur in completing a service. The fuzzy or possibility theory is applied to the knowledge and used to choose the most plausible meaning of the instructions. The approach has been implemented and experimented. Four of the test cases relating to housekeeping services have been presented in this thesis. The results suggest that the collaborative behavior-based approach is successful.
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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 | 0.000 |
| 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 it