Programming an Autonomous Robot Controller by Demonstration Using Artificial Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
The use of Artificial Neural Networks (ANNs) to control autonomous robots has been quite extensively studied. Also, in recent years researchers have begun to investigate the notion of programming such robots using visual programming control models. Some of this work has focused on developing languages based on various programming and robot visual programming-by-demonstration (PBD) systems. Here we extend the latter approach by proposing a visual PBD environment for autonomous robots based on ANNs. Within this environment, sensor-to-motor rules, called sensorimotor maps, are programmed by employing ANNs to match sensor outputs to actuator inputs. The goal is to create a programming environment in which the end-user is not required to have any knowledge of the underlying control model, ANN programming in this case. In this regard, the current proposal appears more promising than previous attempts using the subsumption model.
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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