Toward Human-Robot Interface Standards: Use of Standardization and Intelligent Subsystems for Advancing Human-Robotic Competency in Space Exploration
Bibliographic record
Abstract
<div class="htmlview paragraph">NASA's plans to implement the Vision for Space Exploration include extensive human-robot cooperation across an enterprise spanning multiple missions, systems, and decades. To make this practical, strong enterprise-level interface standards (data, power, communication, interaction, autonomy, and physical) will be required early in the systems and technology development cycle. Such standards should affect both the engineer and operator roles that humans adopt in their interactions with robots. For the engineer role, standards will result in reduced development lead-times, lower cost, and greater efficiency in deploying such systems. For the operator role, standards will result in common autonomy and interaction modes that reduce operator training, minimize workload, and apply to many different robotic platforms. Reduced quantities of spare hardware could also be a benefit of standardization.</div> <div class="htmlview paragraph">This paper discusses the need for, and benefits that derive from, human-robot interface standards from the perspective of stakeholders within NASA, in the academic community, and in industry. The discussion includes an assessment of the scope of the required standards and the extent to which existing standards are applicable and are able to meet the need. In addition, past initiatives to promote standardization and their implications for the current initiative are explored, current efforts to develop appropriate standards are described, and significant gaps and roadblocks are identified.</div>
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".