Why this work is in the frame
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Bibliographic record
Abstract
The goal of this thesis is to address the rendezvous problem for multi-agent coordination and search.To rendezvous is to meet or to physically come together, and the applications of this problem range from everyday life to robotics.Example scenarios for these applications are friends coordinating a meeting or robots and autonomous vehicles coming together for load balancing or resource sharing.Each of these tasks require a rendezvous, which can involve a considerable amount of inter-agent coordination.Our goal is to develop energy-efficient rendezvous algorithms so that we can minimize the inter-agent coordination cost, the number of rendezvous attempts, failure rate, total distance traveled and time taken to rendezvous.We study the multi-agent rendezvous problem under two primary constraints: the amount of prior knowledge about the environment and the agents, and the frequency of communication allowed between the agents.These constraints are combined to define four classes of the rendezvous problem, in different environments with different agents.They are: (a) no prior knowledge and no communication, (b) partial prior knowledge and partial communication, (c) no prior knowledge and partial communication and (d) partial prior knowledge and no communication.We specifically consider four scenarios corresponding to each category respectively, as: (1) rendezvous of simulated agents for resource sharing while exploring an unknown environment without communication, (2) rendezvous between humans on street networks with intermittent communication and uncertainties in travel time, (3) rendezvous search by an Autonomous Underwater Vehicle (AUV) for passively floating scientific data collectors with no prior knowledge about the environment or the agent and periodic communication for motion modeling of the floating targets, and lastly, (4) an Autonomous Surface Vehicle (ASV) searching for lost targets at sea with some prior knowledge about the target's probability distribution and no communication until the target is found.We analyze the rendezvous problem in each of these four cases and present algorithms for them.We validate our proposed rendezvous coordination and search algorithms using software tools that we developed for large scale simulation and testing.This thesis is the result of the great mentorship and extreme patience of my advisor, Prof. Gregory Dudek.He introduced me and many other graduate students to the fundamentals of robotics with his very animated course, "Introduction to Robotics and Intelligent Systems".His timely advice during the course of my Ph.D. program was useful not only in my academic life but will also serve me as lessons for a lifetime.He is a generator of excellent ideas, a modern philosopher and a visionary in the true sense of the word.While he closely guided me in my academic life, he also allowed me to wander at times, to explore new possibilities for learning and growing as a researcher.His positive attitude made some of the most difficult times during my research, just a passing phase.His mentoring combined with great lab facilities and awesome colleagues has made my research a truly wonderful experience.During my program, I had the honour of working with some of the brightest minds in
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it