MARKER-AUGMENTED ROBOT-ENVIRONMENT INTERACTION
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
There has been an increasing interest in developing computational theories of autonomous robots. However, the previous work has focused on intelligent modifications to internal computational structure of a robot, ignoring modifications to external environments. Our work is the first to formalize the modification of an environment by an introduction of markers that replace the internal state. Replacing internal state by addition of markers increases communication through the world. Use of markers has been shown to improve the effectiveness of robots at American Association of Artificial Intelligence robot competitions and RoboCup competitions. We report on the semantics of markers using their logical description and the internal state they replace. We introduce several properties of markers and marker sets like redundancy, mutual exclusivity and efficiency. We show how the stimuli of behaviours can be modified when markers are introduced to replace internal state. We also report on a semi-automatic algorithm that allows robots to place markers in their world. We show how the algorithm can be extended for obtaining a higher replacement of internal state and for handling an autonomous removal of markers. We provide several guidelines for effectively introducing markers in a robot's world.
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.
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 | 0.001 | 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