Editorial: Humanoid Robots for Real-World Applications
Bibliographic record
Abstract
Humanoid Robots for Real-World ApplicationsSince Honda introduced the P2 in 1996, numerous humanoid robots have been developed around the world, and research and development of various fundamental technologies, including bipedal walking, have been conducted.At the same time, attempts have been made to apply humanoid robots to various applications such as plant maintenance, telemedicine, vehicle operation, home security, construction, aircraft manufacturing, disaster response, evaluation of assistive devices, and entertainment.Humanoid robots have an anthropomorphic body, and their major advantage is that they can move within an environment designed for humans and can use tools and vehicles designed for humans as they are.It is hoped that these advantages can be used to help people focus on more creative activities by replacing activities in harsh environments, hazardous tasks, and low addedvalue tasks that people are forced to perform because existing fixed, wheeled, or crawler-type robots are unable to deal with them.In addition, since the fact that something shaped like a human moves like a human has an effect of attracting people, it can be expected to entertain and heal people by interacting with them.It is easier for humans to understand their "intention" through body language.This is related to the avatar application introduced later.Despite these expectations, even today, more than 25 years after the announcement of P2, there is still no humanoid robot that has been put to practical use other than R&D and communication applications.This is because there is no necessity to use humanoid robots in a structured environment like a conventional factory, where existing robots can be easily applied, and the technology is too immature to use humanoid robots in an environment that is so unstructured that existing robots cannot deal with.This Research Topic introduces two efforts to improve the basic capabilities of humanoid robots and one effort to apply humanoid robots to remote services, with the aim of practical applications of humanoid robots.Until now, almost all humanoid robots have used a method in which joints are accurately position-controlled and position commands are updated using joint velocities calculated by inverse kinematics.Recently, methods that updates the position commands by calculating joint accelerations using inverse dynamics calculations, and methods that control the joint torques are being used.Ramuzat et al. implemented these three approaches on the same hardware platform and clarified the advantages and disadvantages of each approach.The method combining position control and inverse kinematics was found to be the least computationally intensive, while the method using torque control was confirmed to have advantages in terms of smoothness of trajectory tracking, energy consumption, and passivity.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.023 | 0.031 |
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".