Survey on Recent Advances in Planning and Control for Collaborative Robotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Collaborative robots (COBOTs) can efficiently assist humans in interactions with robots and the environment when carrying out different tasks. They take advantage of the flexibility and cognitive decision-making skills of humans along with the speed, accuracy, strength, and reliability of robots. Interest in this research area has grown rapidly in recent years and there are applications in many areas, such as smart factories, health services, agriculture, service sector and surveillance. This paper reviews the state-of-the-art of planning and control strategies for collaborative robots. The survey includes various advanced approaches for motion planning, task planning, cooperative bimanual manipulation, cooperative mobile manipulation, learning from demonstration, exoskeletons and conjoined collaboration, and collaborative aerial robotics. A discussion of the challenges and future directions for the proposed research area are presented. This paper offers a comprehensive survey and insight for new researchers in the area.
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.
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 it