A Standardized Benchmark for Humanoid Whole-Body Manipulation
Bibliographic record
Abstract
In this paper we focus on the evaluation of humanoid manipulation skills while balancing on two feet. This involves manipulation while standing and loco-manipulation where the object is being manipulated while taking steps. With this objective in mind, an initial study of whole-body manipulation in a box manipulation scenario with two dif-ferent motions using the University of Waterloo's REEM-C, “Seven”, is investigated to provide insight into a valuable setup, comprehensive test protocols and useful performance metrics based on real world data. The contribution of this paper is a proposed benchmark for whole-body manipulation consisting of the design of a test bed inspired by real use cases for humanoid whole-body manipulation tasks, the definition of a set of protocols to standardize the testing procedure and insightful key performance indicators (KPIs) based on this initial study with the real robot. The proposed benchmark for humanoid whole-body manipulation is part of the EUROBENCH project that aims at creating a benchmarking framework for robotic systems performing locomotion related tasks.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".