TARGET TRACKING ROBOTIC MANIPULATION THEORIES APPLIED TO FORCE/POSITION CONTROL IN PEG-IN-HOLE ASSEMBLY TASKS
Bibliographic record
Abstract
One of the most common tasks in robotic assembly processes is to insert a peg into a hole. On the surface this task seems to be simple and straight forward. However, in order to perform this process successfully it requires a complex interaction between the force and geometry of the robot and environment. Typical industrial robots have sensors at the joints and wrist to monitor the hand position and force during this interaction. Using this sensor information, many manipulation methodologies can be considered to attempt the peg insertion task. Hybrid control is one such compliant control scheme that is commonly used in industry. Nevertheless its theoretical feasibility has stirred much controversy. Recent developments in target tracking for force/position control have made it a viable alternative to hybrid control. This paper compares the performance of target tracking to the already established hybrid control schemes by assessing the outcome of case studies of several simulation experiments of peg-in-hole assembly tasks. The results indicate that the robot inserts the peg into the hole faster and with less friction force by using target tracking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".