Papers
10
Total Citations
166
H-Index
7
About
Malcolm Doering is a leading researcher in human-robot interaction (HRI), specializing in situated dialogue, social imitation learning, and embodied communication. His work bridges the gap between how humans and robots perceive and act within shared environments, with a particular focus on referential communication and social behavior learning. Doering’s most influential contribution is his pioneering research on embodied collaborative models for referring expression generation (REG), which enables robots to dynamically adapt their descriptions of objects based on the human’s perspective—a challenge central to natural HRI. His 2015 paper on this topic has garnered 60 citations, underscoring its foundational impact. Beyond REG, Doering has advanced data-driven imitation learning, developing techniques that allow robots to autonomously acquire social interaction logic from human-human interaction data, as seen in his 2019 work on modeling interaction structure (30 citations). He has also explored curiosity-driven learning, neural-network-based memory, and error awareness in HRI, pushing toward more adaptive and socially aware robots. Notably, his recent research on shopkeeper robots addresses practical challenges like adapting to changing product information and conveying object properties through manipulation. Doering’s work is distinguished by its focus on minimally supervised, scalable learning systems that enhance robot social competence, making him a key figure in the quest for truly interactive and context-aware robotic assistants.
Research Focus
Key Achievements
Top Papers
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- 4Curiosity Did Not Kill the Robot17 citations · 2019
- 5Neural-network-based Memory for a Social Robot9 citations · 2019
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- 9Zero-Shot Learning to Enable Error Awareness in Data-Driven HRI3 citations · 2024
- 10Communicating Physical Properties Through Robot Object Manipulation2 citations · 2025