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Record W4323637267 · doi:10.1145/3568294.3580160

Where is My Phone?

2023· article· en· W4323637267 on OpenAlexaff
Juhi Shah, Ali Ayub, Chrystopher L. Nehaniv, Kerstin Dautenhahn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceHuman–computer interactionRobotSalientPhoneFeature (linguistics)Mobile robotTrack (disk drive)Interface (matter)Artificial intelligence

Abstract

fetched live from OpenAlex

Persons with Dementia face the issue of a deteriorating memory. As assistive robots are being increasingly adapted as a helper to persons with dementia, this paper presents an additional feature to such robots. Assistive robots that might assist with different tasks in users' households can also be utilized to track salient objects to quickly find them in case they are misplaced. This paper presents an episodic memory system that can enable a robot to recognize salient objects and track them while moving in and out of the environment. We also demonstrate how to develop access to the robot's memory in an easy-to-understand way using a graphical user interface (GUI). The proposed system is integrated with a Fetch mobile manipulator robot to track, store and visualize various household objects in an environment. Results from a system evaluation study are encouraging and the system will be further investigated in future co-design and user studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.229
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1800.148

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.

Opus teacher head0.071
GPT teacher head0.422
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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