Evaluating Data Representations for Object Recognition During Pick-and-Place Manipulation Tasks
Bibliographic record
Abstract
When manipulating objects, robots need to build a local and global description of the environment simultaneously. Recognizing objects and estimating their pose are examples of tasks expected from robots when operating in unstructured environments. An efficient solution to these tasks has the potential to increase robotic usage in such settings. This paper presents a study on the representation of tactile and joint-position data to recognize everyday objects. We performed 12 different experiments extracting features in different ways from a publicly available dataset. More specifically, this work uses three data representations, namely 3 Points, 10 Points, Average and Descriptive Statistics (DS) over 2 different sensor types (i.e., positional and tactile sensors separately) and the combination of both. Using these data representations, we trained and evaluated machine learning models in the object recognition task. Our findings support that tactile data and its combination with finger joint position information can be successfully used for object identification during manipulation tasks. The feature engineering approach used to represent the dataset used in this paper showed promising results regarding recognizing objects using a combination of tactile and joint-position information. Our exploratory analysis testing different data representations was crucial for improving objects’ recognition starting from a low of accuracy 30.31% (using data from the positional sensor only with sampled averages) to a high performance of 93.53% accuracy (using an Extra Tree classifier trained on data from all sensors with a DS representation).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".