Robotic Grasping and Manipulation Competition at the 2024 IEEE/RAS International Conference on Robotics and Automation [Competitions]
Bibliographic record
Abstract
The Ninth Robotic Grasping and Manipulation Competition (RGMC) took place in Yokohama, Japan, during the 2024 IEEE/RAS International Conference on Robotics and Automation (ICRA). The series of RGMC events started in 2016 at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) with strong support from the conference’s organization committee, and since then they have been held each year at ICRA or IROS [1]. Across the editions, RGMC engaged the community in solving the open challenges associated with various robotic grasping and manipulation tasks for manufacturing, service robots, and logistics, and in advancing research and technology towards more realistic scenarios that can be encountered in daily activities at home or in warehouses. These tasks include assembling and disassembling boards, hand-in-hand grasping, picking and placing various objects, pouring liquids into a cup, bin picking, rearranging and setting formal tables, folding and unfolding cloths, and receiving objects handed over by a person. The goal of RGMC across these tasks is to assess the autonomous manipulation capabilities of a robotic arm when dealing with unknown or novel objects with varying physical properties and when handling scenarios with various degrees of uncertainty caused by a cluttered scene, random initial configurations, or human behaviors when interacting with the robot. For example, objects can vary in their shapes, appearances, transparency, deformability, and weight.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.023 |
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".