Singularity Tolerant Robotic Manipulation for Autonomous Field Tracking in an Unknown Environment
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a comprehensive motion control strategy for the autonomous operation of robotic manipulators combined with the sensor-driven recursive estimation of an unknown field of interest. The spatial distribution of the environmental phenomenon is modeled by a radial basis function (RBF) network and their weight parameters are estimated by a recursive least square (RLS) method using the collective measurements from the on-board sensors mounted to the manipulator. The asymptotic tracking has been simultaneously achieved by the control law based on the gradient of the estimated field. Since the target location cannot be known a priori, the motion controller has to be designed in explicit consideration of tolerating the singular configuration of the manipulator kinematics. By using the null space decomposition of the task space for the Jacobian near the singularities, a systematic method is suggested to command the task space control law in spite of the singular configurations. Simulation results using the three link planar robot are presented to support the main ideas.
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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 | 0.001 |
| 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