Active estimation of object dynamics parameters with tactile sensors
Why this work is in the frame
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Bibliographic record
Abstract
The estimation of parameters that affect the dynamics of objects—such as viscosity or internal degree of freedom—is an important step in autonomous and dexterous robotic manipulation of objects. However, accurate and efficient estimation of these object parameters may be challenging due to complex, highly nonlinear underlying physical processes. To improve on the quality of otherwise hand-crafted solutions, automatic generation of control strategies can be helpful. We present a framework that uses active learning to help with sequential gathering of data samples,using information-theoretic ciriteria to find the optimal actions to perform at each time step. We demonstrate the usefulness of our approach on a robotic hand-arm setup, where the task involves shaking bottles of different liquids in order to determine the liquid's viscosity from only tactile feedback. We optimize the shaking frequency and the rotation angle of shaking in an online manner in order to speed up convergence of estimates.
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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 it