Human-to-robot skill transfer using the SPORE approximation
Why this work is in the frame
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Bibliographic record
Abstract
We propose a framework for programming robotic tasks using human-to-robot skill transfer. We assume that there exists a human expert who can accomplish a task in an unstructured environment by using various sensor displays and controls. The human expert performs the desired task a number of times while his/her input/output pairs are being recorded by the robot. The robot then uses this recorded data to construct a mapping between these sensor inputs and actuator outputs. This mapping must be general enough to allow the robot to accomplish the same task, in similar but not identical, dynamic, unstructured environments. This paper presents a testbed for human-to-robot skill transfer which is based on the teleoperated control of a small mobile robot working in an unstructured environment. The skill which is transferred from human-to-robot is loosely based on the tree tending task, a task which was chosen for its inherently unstructured nature. The SPORE approximation is proposed as a means for creating the robot's mapping from sensor inputs to actuator outputs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it