A Multiple Working Mode Approach to Hammering with a Modular Reconfigurable Robot
Bibliographic record
Abstract
Robot joints could be damaged by impulsive forces due to impact when the robot performs hammering operations. This paper presents a strategy that allows a modular and reconfigurable robot to safely perform nail hammering in 3D space without hardware enhancements. To do this, a multiple working mode approach is applied to switch the selected joint(s) to passive mode with friction compensation to allow free rotation during impact. Analytic impulse models are used to predict joint impulses which can be computed offline or online and serve as criteria for mode switching. End-effector pose, and link momentum are proposed as criteria to track and time the switching. The strategy assumes multiple swings are performed with the last swing resulting in a collision with the board or wall. Joint impulses are constrained in case the hammer collides with such an object of infinite effective mass. Advantages of the proposed approach may include savings on space, weight, costs, and complexity for a limited range of nail/board environments. An experimental study validates analytic models of hammering and effectiveness of multiple working mode approach.
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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 | 0.000 |
| 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".