ArticleMediterranean Conference on Control & Automation : [proceedings]. IEEE Mediterranean Conference on Control & Automation2017
Learning models of Human-Robot Interaction from small data.
Article in Mediterranean Conference on Control & Automation : [proceedings]. IEEE Mediterranean Conference on Control & Automation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- HRI-confusion: A multimodal dataset for modelling and detecting user confusion in situated human-robot interaction.Data in brief · 2025Article
- UpStory: the uppsala storytelling dataset.Frontiers in robotics and AI · 2025Article
- Resilient Supervisory Multi-Agent Systems.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2022Article
- Non-Smooth Control Barrier Navigation Functions for STL Motion Planning.Frontiers in robotics and AI · 2022Article
- A Hybrid PAC Reinforcement Learning Algorithm for Human-Robot Interaction.Frontiers in robotics and AI · 2022Article
- GEARing smart environments for pediatric motor rehabilitation.Journal of neuroengineering and rehabilitation · 2020Article
- Statistical Relational Learning With Unconventional String Models.Frontiers in robotics and AI · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed. The paper adopts a Markov decision process (MDP) as such a model, and selects the transition probabilities through an empirical approximation procedure called smoothing. Smoothing has been successfully applied in natural language processing (NLP) and identification where, similarly to the current paradigm, learning from small data sets is crucial. The goal of this paper is two-fold: (i) to describe our application of HRI, and (ii) to provide evidence that supports the application of smoothing for small data sets.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.