Toward autonomous disassembling of randomly piled objects with minimal perturbation
Bibliographic record
Abstract
Autonomous capabilities for manipulating randomly piled objects may enhance current methods of path planning and open a new field of development for mobile manipulation and Urban Search And Rescue (USAR) robotics. This paper introduces the challenge of achieving such manipulation capabilities and as a first step presents three algorithms, including a proposed novel solution, for the selection of objects to remove from a pile. The proposed algorithm determines a removability rank for each object according to the degree of its encapsulation within other objects. Using the contact vectors of the examined object, it is possible to obtain the motions that will not violate the object's unilateral contact constraints. The removability rank of the object is proportional to the union of all such motions. All algorithms were tested in simulation in full and partial knowledge modes, and evaluated on a physical robot with a simple manipulator and sensor. This work contributes: the introduction of an important autonomous manipulation challenge, the solution of which will be useful in the field of manipulation in general and USAR in particular; a specific novel algorithm for the construction of disassembly plans for piled objects; and an experimental evaluation of three algorithms targeted at such construction.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".