FusionBot: A barista robot: Fusionbot serving coffees to visitors during technology exhibition event
Bibliographic record
Abstract
Summary form of only given: This video shows a service robot named FusionBot autonomously serving coffees to visitors on their request, which occurred during two days-long experiment in TechFest 2008 event. The coffee serving task involves taking coffee order from a visitor, identifying a cup and smart coffee machine, moving towards the coffee machine, communicating with the coffee machine and fetching the coffee cup to the visitor. The main purpose of this experiment is to explore and demonstrate the utility of an interactive service robot in smart home environment, thereby improving the quality of human life. Before conducting the experiments, visitors were given general procedural instructions and simple introduction on how the FusionBot works. Visitors then performed experiment tasks, i.e., ordering a cup of coffee. Thereafter, the visitors were asked to fill out the satisfaction questionnaires to find out their reaction and perception on the FusionBot. Of just over 100 survey questionnaires handed out, sixty eight (68) valid responses (i.e. 68%) were received. Over all, with regards to the FusionBot task satisfaction, more than half of respondents were satisfied with what the FusionBot can do. Nearly one quarter of the respondents indicated that it was not easy to communicate with the FusionBot. This could be due to occurrence of various background noises, which were falsely picked up by the FusionBot as speech input from the visitor. Similarly, less than one quarter indicated that it was not easy to learn how to use the FusionBot. This could be due to the not knowing what to do with the FusionBot and not knowing what the FusionBot does. The experiment was successful in two main dimensions; (1) the robot demonstrated the ability to interact with visitors and perform challenging real-world task autonomously, and (2) It provided some evidence towards the feasibility of using autonomous service robot and smart coffee machine to serve drink in a reception/home or acting as a host in an organization. While preliminary, the experiment also suggests that while developing a service robot; (1) static appearance is very important, (2) requires robust speech recognition and vision understanding, and finally (3) requires comprehensive training on speech and vision with respective data.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.015 |
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".