Papers
6
Total Citations
84
H-Index
5
About
T. Nishida is a pioneering researcher in human-robot interaction and multi-agent systems, whose work bridges the gap between autonomous navigation and natural human-robot communication. Their most influential contribution is the development of Augmented Bayesian Networks for learning interaction protocols, applied to guided robot navigation—a paper that has garnered 37 citations and addresses the critical challenge of enabling robots to understand natural human gestures without requiring constant human intervention. Nishida’s foundational work on ontology-based cooperative environments for real-world agents (19 citations) established frameworks for heterogeneous agents to share knowledge and communicate effectively, laying groundwork for modern multi-robot systems. Their research uniquely explores entrainment-based interaction (9 citations), using synchronization and dynamical systems to allow humans to convey tacit intentions to agents through bodily expressions. Nishida has also contributed to computational emotional models for human-robot interaction, developing simulations like “The Panic Room” that explore synthetic emotions. Their work on self-organizing maps combined with reinforcement learning (8 citations) advanced the acquisition of vision-action relationships for learning agents. Through these diverse contributions, Nishida has shaped how robots perceive, learn from, and naturally interact with humans in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2An Ontology-based Cooperative Environment for Real-world Agents19 citations · 1996
- 3Entrainment based human-agent interaction9 citations · 2005
- 4
- 5From Computational Emotional Models to HRI7 citations · 2013
- 6Robot and human communication using bodily expressions4 citations · 2003