Nullspace Control of Robotic Arm Task Prioritization Considering Emotional Information
Bibliographic record
Abstract
In order to make the human-robot interaction more anthropomorphic, so that the robot expresses interaction actions with emotional information during the interaction, a trajectory optimised control method of the robot arm is proposed to express the emotional factors. They include an emotional kinematics feature mapping part, a multitasking priority setting part, and a zero-space control part. The eight three-dimensional emotional mappings in the Pleasure-Arousal-Dominance (PAD) emotion model are mapped to the three kinematic features of the joints of the robotic arm, and then the emotional information influences the amplitude, jitteriness, and end-effector offset of the interaction action by the zero-space control method in the joint space. The method ensures that the normal interaction task is performed by the zero-space characteristic, while the emotional information is expressed by the motion characteristics, which enables the robot to generate interaction actions with different emotional information. It solves the problem of unclear connection between emotional information and robot arm movement expression identified in previous studies. Taking hand waving as an example, we simulate two kinds of iconic emotions and normal hand waving movements, and show that the generated robot arm movements can better express different emotions and achieve better human-robot interaction.
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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) | 0.000 | 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".