Utilizing Foundation Models to Enhance Autonomous Robotic Systems in Dynamic and Unstructured Environments
Bibliographic record
Abstract
Autonomous robotics in unstructured environments,such as disaster zones and construction sites, presents significantchallenges due to unpredictability and complexity. Traditionalrobotic systems often struggle with adaptability and generalization in these settings. Recent advancements in foundationmodels, including Large Language Models (LLMs) and LargeVision Models (LVMs), offer promising solutions by enhancingperception, decision-making, and interaction capabilities. Thispaper explores the integration of foundation models into autonomous robotics, systematically reviewing current applications,identifying challenges, and proposing future research directions.We analyze the impact of these models on various robotic tasks,assess the current level of autonomy achieved, and envisionscenarios for fully autonomous operations. Our findings indicatethat while foundation models significantly improve cognitivetasks, their application in physical interactions remains nascent.This study serves as a comprehensive benchmark for futureadvancements in autonomous robotics within dynamic and unstructured environments
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".