Towards a robust framework for visual human-robot interaction
Bibliographic record
Abstract
This thesis presents a vision-based interface for human-robot interaction and control for autonomous robots in arbitrary environments. Vision has the advantage of being a low-power, unobtrusive sensing modality. The advent of robust algorithms and a significant increase in computational power are the two most significant reasons for such widespread integration. The research presented in this dissertation looks at visual sensing as an intuitive and uncomplicated method for a human operator to communicate in close-range with a mobile robot. The array of communication paradigms we investigate includes, but are not limited to, visual tracking and servoing, programming of robot behaviors with visual cues, visual feature recognition, mapping and identification of individuals through gait characteristics using spatio-temporal visual patterns and quantifying the performance of these human-robot interaction approaches. The proposed framework enables a human operator to control and program a robot without the need for any complicated input interface, and also enables the robot to learn about its environment and the operator using the visual interface. We investigate the applicability of machine learning methods – supervised learning in particular – to train the vision system using stored training data. A key aspect of our work is a system for human-robot dialog for safe and efficient task execution under uncertainty. We present extensive validation through a set of human-interface trials, and also demonstrate the applicability of this research in the field on the Aqua amphibious robot platform in the under water domain. While our framework is not specific to robots operating in the under water domain, vision under water is affected by a number of issues, such as lighting variations and color degradation, among others. Evaluating the approach in such difficult operating conditions provides a definitive validation of our approach.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".