Showing Robots How to Follow People using a Broomstick Interface
Bibliographic record
Abstract
Figure 1 –During demonstration the primary person walks as they normally would in the given circumstance and the designer pushes a robot (iRobot Roomba) on a broomstick to demonstrate the style in which the robot should follow the primary person. Following, during generation the primary person walks normally and the interactive robot path and style is automatically generated in real-time to react to the primary person in a way that matches the demonstration style. Abstract—Robots are poised to enter our everyday environments such as our homes and offices, contexts that present unique questions such as the style of the robot’s actions. Style-oriented characteristics are difficult to define programmatically, a problem that is particularly prominent for a robot’s interactive behaviors, those that must react accordingly to dynamic actions of people. In this paper, we present a technique for programming the style of how a robot should follow a person by demonstration, such that non-technical designers and users can directly create the style of following using their existing skill sets. We envision that simple physical interfaces like ours can be used by nontechnical people to design the style of a wide range of robotic behaviors. I.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.008 | 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; both teacher heads agree on what is shown here.
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".