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- research-articleMarch 2024
What Is Your Other Hand Doing, Robot? A Model of Behavior for Shopkeeper Robot's Idle Hand
HRI '24: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot InteractionPages 552–560https://doi.org/10.1145/3610977.3634986In retail settings, a robot's one-handed manipulation of objects can come across as thoughtless and impolite, thus creating a negative customer experience. To solve this problem, we first observed how human shopkeepers interact with customers, ...
- research-articleMarch 2024
Zero-Shot Learning to Enable Error Awareness in Data-Driven HRI
HRI '24: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot InteractionPages 592–601https://doi.org/10.1145/3610977.3634940Data-driven social imitation learning is a minimally-supervised approach to generating robot behaviors for human-robot interaction (HRI). However, this type of learning-based approach is error-prone. Existing error detection methods for HRI rely on data ...
- research-articleJuly 2021
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
ACM Transactions on Human-Robot Interaction (THRI), Volume 10, Issue 4Article No.: 31, Pages 1–20https://doi.org/10.1145/3451883Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that ...
- research-articleMarch 2020
Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data
HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot InteractionPages 419–427https://doi.org/10.1145/3319502.3374798We envision a future where service robots autonomously learn how to interact with humans directly from human-human interaction data, without any manual intervention. In this paper, we present a data-driven pipeline that: (1) takes in low-level data of a ...
- research-articleNovember 2019
Neural-network-based Memory for a Social Robot: Learning a Memory Model of Human Behavior from Data
ACM Transactions on Human-Robot Interaction (THRI), Volume 8, Issue 4Article No.: 24, Pages 1–27https://doi.org/10.1145/3338810Many recent studies have shown that behaviors and interaction logic for social robots can be learned automatically from natural examples of human-human interaction by machine learning algorithms, with minimal input from human designers [1--4]. In this ...
- research-articleJuly 2019
Curiosity Did Not Kill the Robot: A Curiosity-based Learning System for a Shopkeeper Robot
ACM Transactions on Human-Robot Interaction (THRI), Volume 8, Issue 3Article No.: 15, Pages 1–24https://doi.org/10.1145/3326462Learning from human interaction data is a promising approach for developing robot interaction logic, but behaviors learned only from offline data simply represent the most frequent interaction patterns in the training data, without any adaptation for ...
- research-articleMarch 2015
Embodied Collaborative Referring Expression Generation in Situated Human-Robot Interaction
HRI '15: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot InteractionPages 271–278https://doi.org/10.1145/2696454.2696467To facilitate referential communication between humans and robots and mediate their differences in representing the shared environment, we are exploring embodied collaborative models for referring expression generation (REG). Instead of a single minimum ...
- ArticleJuly 2014
Collaborative models for referring expression generation in situated dialogue
In situated dialogue with artificial agents (e.g., robots), although a human and an agent are co-present, the agent's representation and the human's representation of the shared environment are significantly mismatched. Because of this misalignment, our ...