Autonomous Mobile Systems for Long-Term Operations in Spatio-Temporal Environments
Bibliographic record
Abstract
This document reports on research conducted between 2001 and 2015 in the field of au-tonomous mobile robotics, specifically in what became known “field robotics”: a focus ofrobotics on outdoor, little-structured environments close to industrial applications. Chap-ter 2 describes a number of research projects, starting with activities initiated during mydoctorate research at INRIA between 2001 and 2004, followed by a post-doctoral fellowshipat CSIRO, in Canberra and Brisbane, Australia between 2004 and 2007. From 2007 to 2012,my role as Deputy-Director of the Autonomous Systems Lab at ETH Z ̈urich, Switzerland,gave me the opportunity to supervise a number of projects ranging from space robotics andmechatronic design to European projects on indoor navigation or autonomous driving. Onthe other hand, the last chapter will describe my reseach plan stemming from this experience andpreliminary results from projects started in my current position as Associate Professor atGeorgiaTech Lorraine, the French campus of the Georgia Institute of Technology, also knownas GeorgiaTech, located in Atlanta, USA
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".