Action-Inclusive Multi-Future Prediction Using a Generative Model in Human-Related Scenes for Mobile Robots

Mobility in daily unstructured environments, particularly in human-centered scenarios, remains a fundamental challenge for mobile robots. While traditional prediction-based approaches primarily estimate partial features for robot decision making, such as position and velocity, recent world models en...

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Veröffentlicht in:	IEEE access Jg. 13; S. 167034 - 167044
Hauptverfasser:	Xu, Chenfei, Ahmad, Huthaifa, Okadome, Yuya, Ishiguro, Hiroshi, Nakamura, Yutaka
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Behavioral sciences Data models Data sets for robot learning deep learning methods human-aware motion planning human–robot interaction Mobile robots Planning Predictive models Robot sensing systems Robots Transformers Videos Visualization
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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