Humanoid Robot Imitation with Pose Similarity Metric Learning
Bibliographic record
Abstract
Imitation is considered to be a kind of social learning that allows the transfer of information, actions, behaviours, etc. Whereas current robots are unable to perform as many tasks as human, it is a natural way for them to learn by imitations, just as human does. With the humanoid robots being more intelligent, the field of robot imitation has getting noticeable advance. In this paper, we focus on the pose imitation between a human and a humanoid robot and learning a similarity metric between human pose and robot pose. In contrast to recent approaches that capture human data using expensive motion captures or only imitate the upper body movements, our framework adopts a Kinect instead and can deal with complex, whole body motions by keeping both single pose balance and pose sequence balance. Meanwhile, different from previous work that employs subjective evaluation, we propose a pose similarity metric based on the shared structure of the motion spaces of human and robot. The qualitative and quantitative experimental results demonstrate a satisfactory imitation performance and indicate that the proposed pose similarity metric is discriminative.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".