Humanoid navigation and heavy load transportation in a cluttered environment
Bibliographic record
Abstract
Although in recent years several studies aimed at the navigation of robots in cluttered environments, just a few have addressed the problem of robots navigating while moving a large or heavy object. This is especially useful when transporting loads with variable weights and shapes without having to change the robot hardware. On one hand, a major advantage of using a humanoid robot to move an object is that it has arms to firmly grasp it and control it. On the other hand, humanoid robots tend to have higher drift than their wheeled counterparts as well as having significant lateral swing while walking, which propagates to anything they carry. In this work, we present algorithms for a humanoid robot navigating in a cluttered environment while pushing a cart-like object. In addition, the algorithms make use of the hands and arms to articulate the cart when executing tight turns using whole body control scheme to reduce the lateral swing effect on the load and ensure a safe transport. Experiments conducted on a real Nao robot assessed the proposed approach and algorithms, they show that the payload of a humanoid robot can be significantly increased without changing the humanoid robot's hardware, and therefore enact the capacity of humanoid robots in real-life situations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".