Malcolm Doering
Biographic Data
| ID | 5121707 |
|---|---|
| NAME | Malcolm Doering |
| GIVEN NAMES | Malcolm |
| FAMILY NAME | Doering |
| SIGNATURE | DOERING M |
| AFFILIATIONS | Kyoto University |
| ORCID | 0000-0002-2805-0187 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Exploration of Pointing Forms in Customer–Shopkeeper Interactions
To investigate the use of different pointing forms in service scenarios, we collected the ShopPoint dataset, a skeleton-based dataset of pointing gestures from customer–shopkeeper interactions in a camera shop scenario. Thirteen participants took part in the data collection, including 3 shopkeepers with real-world customer service experience and 10 customers. We recorded 61 one-to-one role-played interactions. Coders annotated pointing gestures f…
Online Continual Learning for Automatic Error Prevention in Data-Driven HRI
Data-driven social imitation learning is a minimally supervised approach to generating robot behaviors for human–robot interaction (HRI). However, like any learning-based approach, there are inherent challenges and potential errors. Existing strategies for error prevention in HRI predominantly rely on handcrafted methods and predefined design guidelines, with limited focus on learning-based approaches. In this article, we present an autonomous, o…
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Data-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 uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/…
Curiosity Did Not Kill the Robot
Learning 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 individual differences. We developed a robot that incorporates both data-driven and interactive learning. Our robot first learns high-level dialog and spatial behavior patterns from offline examples …
Curiosity Did Not Kill the Robot
Learning 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 individual differences. We developed a robot that incorporates both data-driven and interactive learning. Our robot first learns high-level dialog and spatial behavior patterns from offline examples …
Curiosity Did Not Kill the Robot
Learning 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 individual differences. We developed a robot that incorporates both data-driven and interactive learning. Our robot first learns high-level dialog and spatial behavior patterns from offline examples …
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Data-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 uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/…
Exploration of Pointing Forms in Customer–Shopkeeper Interactions
To investigate the use of different pointing forms in service scenarios, we collected the ShopPoint dataset, a skeleton-based dataset of pointing gestures from customer–shopkeeper interactions in a camera shop scenario. Thirteen participants took part in the data collection, including 3 shopkeepers with real-world customer service experience and 10 customers. We recorded 61 one-to-one role-played interactions. Coders annotated pointing gestures f…
Online Continual Learning for Automatic Error Prevention in Data-Driven HRI
Data-driven social imitation learning is a minimally supervised approach to generating robot behaviors for human–robot interaction (HRI). However, like any learning-based approach, there are inherent challenges and potential errors. Existing strategies for error prevention in HRI predominantly rely on handcrafted methods and predefined design guidelines, with limited focus on learning-based approaches. In this article, we present an autonomous, o…
Artificial Intelligence (3 works) · Computer Science (3 works) · Human–computer interaction (3 works) · Social Robot Interaction and HRI (3 works) · Machine learning (2 works) · Reinforcement Learning in Robotics (2 works) · Robot (2 works) · Adaptation (eye (1 work) · Annotation (1 work) · Anomaly Detection Techniques and Applications (1 work)