Style by Demonstration: Using Broomsticks and Tangibles to Show Robots How to Follow People
Bibliographic record
Abstract
The style in which a robot moves, including its gait or locomotion style, can project strong messages, for example, it can be easy to distinguish a happy dog from an aggressive dog simply by how it is moving, and one can often tell if a colleague is stressed simply by the way they are walking. Defining the real-time interactive, stylistic aspects of robotic movements via programming can be difficult and time consuming. Instead, we propose to enable people to use their existing teaching skills to directly demonstrate to robots the desired style of robot movements; in this paper we present an initial style-bydemonstration (SBD) proof-of-concept that focuses on teaching a robot specific, interactive locomotion styles. We present a novel broomstick-robot interface for directly demonstrating locomotion style to a robot, and a design critique by experienced programmers that compares the designing of interactive, stylistic robotic locomotion by our Style-By-Demonstration (SBD) approach with traditional programming methods.
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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 | 0.000 | 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) | 0.009 | 0.001 |
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".