Natural head and body orientation for humanoid robots during conversations with moving human partners through motion capture analysis
Bibliographic record
Abstract
In conversations between humans, a natural body and head orientation towards the interlocutor is important for their social interaction. Humanoids communicating with humans have to learn how to orient themselves properly which becomes a challenging task in the case of moving conversation partners. Studies of conversational behaviour often involve only stationary partners. In this research, we perform a motion capture study to address the scenario of moving subjects. Specifically, study trials were recorded during conversation between a human participant and interlocutor, with a focus on the behaviour of the head, shoulders, and feet. The results help better understand how humans behave while conversing with non-stationary interlocutors. The data from the trials was used to generate a mathematical model describing the relationship of the angle at which the interlocutor is located to the orientations of the head, shoulders and feet while tracking is performed. A new model setup to couple the motion of the interlocutor, the head and the shoulders is introduced, as well as a model to represent stepping in order to better replicate participant behaviour. The models are evaluated and then deployed to the REEM-C Humanoid Robot, for the purposes of generating a natural behavior of the robot and improving human-robot interaction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".