Robotic Episodic Learning and Behaviour Control Integrated with Neuron Stimulation Mechanism
Bibliographic record
Abstract
There are problems of curse of dimensionality and perceptual aliasing in robot behaviour control under uncertainty. To solve the problem, a framework called episodic memory-driving Markov decision process(EM-MDP) is proposed by introducing neuron stimulation mechanism, in order to achieve environmental experience self-learning and behaviour control under multi-source uncertainty. Firstly, an episodic memory model is built, and an activation and organization mechanism of state neurons is proposed based on cognitive neuroscience. Secondly, self-learning of episodic memory is realized by utilizing adaptive resonance theory(ART) and sparse distributed memory(SDM) through Hebbian rules. A robot behaviour control strategy is established by neuron synaptic potential. Robot can evaluate the past events sequence, predict the current state and plan the desired behaviour. Finally, the experimental results show that the model and control strategy can achieve the objectives of robot behaviour control in universal scenes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".