Shaping Affective Robot Haru’s Reactive Response
Bibliographic record
Abstract
We describe a method of teaching a robot its empathic behavioural response from its interaction with people. We used the input modalities such as relative spatial information, facial expressions, body gestures and speech information as perception input that triggers the robot’s empathic response. First, we bootstrap the training through a pre-learning mechanism in which training is conducted by users who know the robotic system. This phase provides simulation-based training using a simple graphical user interface to simulate the input, rewards and correction feedback. In the second phase, we developed an online learning scheme for naive users to personalize their robot further, building on top of the bootstrapped model. Here, we developed a natural user interface that enables natural human-robot interaction via the suite of sensors that allows the users to provide evaluative feedback during the interaction with the robot. We evaluated the system and our results show that bootstrapping is an efficient tool to hasten the robot’s learning while online learning provided some form of personalization in the real environment with naive users.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".