Touch Semantics for Intuitive Physical Manipulation of Humanoids
Bibliographic record
Abstract
Rather than systematically programming joint or task trajectories, having a human physically manipulate the robot for direct adjustments is more intuitive, saves time, and increases usability, especially for nonexperts. Interactive motion generation or repositioning of humanoid robots through direct human-touch manipulation is not an easy task, especially for high-level multijoint maneuvers. We propose a set of design rules for generating intuitive touch semantics called the “two-touch kinematic chain paradigm.” Our method interprets user touch intentions to allow motions ranging from low-level single joint control to high-level whole-body task control with posture generation, stepping, and walking. The goal is to provide the user with an intuitive protocol for physical humanoid manipulation that can serve the purpose of any application. The generated set of touch semantics is embodied in a finite state machine-based framework using a task-space quadratic programming controller to interpret human touch using capacitive sensors embedded in the humanoid shell, and force-torque sensors located at the ankles and wrists. A position-controlled humanoid robot is used to assess the utility and function of our proposed touch semantics for physical manipulation. Furthermore, a user study with nonexperts examines how our approach is perceived in practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".