Sensor Observability Analysis for Maximizing Task-Space Observability of Articulated Robots
Bibliographic record
Abstract
We propose a novel performance metric for articulated robots with distributed directional sensors called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sensor observability analysis</i> (SOA). These robot-mounted distributed directional sensors (e.g., joint torque sensors) change their individual sensing directions as the joints move. SOA transforms individual sensor axes in joint space to provide the cumulative sensing quality of these sensors to observe each task-space axis, akin to forward kinematics for sensors. For example, certain joint configurations may align joint torque sensors in such a way that they are unable to observe interaction forces in one or more task-space (e.g., Cartesian) axes. The resultant sensor observability performance metrics can then be used in optimization and in null-space control to avoid sensor observability in singular configurations or to maximize sensor observability in particular directions. We use the specific case of force sensing in serial robot manipulators to showcase the analysis. Parallels are drawn between sensor observability and traditional kinematic manipulability; SOA is shown to be more generalizable in terms of analyzing non-joint-mounted sensors and can potentially be applied to sensor types other than for force sensing. Simulations and experiments using a custom 3-degree of freedom robot and the Baxter robot demonstrate the utility and importance of sensor observability in physical interactions.
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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 | 0.001 |
| 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".