Robotic autonomous behavior selection using episodic memory and attention system
Bibliographic record
Abstract
Purpose This paper aims to focus on the autonomous behavior selection issue of robotics from the perspective of episodic memory in cognitive neuroscience with biology-inspired attention system. It instructs a robot to follow a sequence of behaviors. This is similar to human travel to a target location by guidance. Design/methodology/approach The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron stimulation mechanism. Based on the learned episodic memory, the robotic global planning method is proposed for efficient behaviors sequence prediction using bottom-up attention. Local behavior planning based on risk function and feasible paths is used for behavior reasoning under imperfect memory. Aiming at the problem of whole target selection under redundant environmental information, a top-down attention servo control method is proposed to effectively detect the target containing multi-parts and distractors which share same features with the target. Findings Based on the proposed method, the robot is able to accumulate experience through memory, and achieve adaptive behavior planning, prediction and reasoning between tasks, environment and threats. Experimental results show that the method can balance the task objectives, select the suitable behavior according to current environment. Originality/value The behavior selection method is integrated with cognitive levels to generate optimal behavioral sequence. The challenges in robotic planning under uncertainty and the issue of target selection under redundant environment are addressed.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".