Peixin Chang
Papers
7
Total Citations
226
H-Index
4
About
Peixin Chang is a robotics researcher whose work sits at the intersection of autonomous navigation, human-robot interaction, and multimodal learning. His research focuses primarily on enabling mobile robots to navigate safely and intelligently in complex, human-populated environments — a challenge that demands both perceptual sophistication and real-time decision-making. Chang's most influential contribution is his work on decentralized structural recurrent neural networks for crowd navigation using deep reinforcement learning, which has garnered 118 citations and addressed critical limitations in partially observable settings where agent dynamics are unknown. Building on this foundation, he advanced the field further with intention-aware navigation using attention-based interaction graphs (76 citations), enabling robots to model diverse agent interactions and anticipate human intentions in dense crowds. Beyond navigation, Chang has pioneered multimodal robot control, investigating how robots can interpret sound and visual signals together for more natural human-robot communication. His DRAGON system (18 citations) demonstrates a dialogue-based assistive robot for visually impaired individuals, combining visual-language grounding with semantic navigation. With over 200 cumulative citations, Chang's body of work reflects a consistent commitment to making robots more perceptive, adaptive, and genuinely useful in human-centered environments.
Research Focus
Key Achievements
Top Papers
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- 5Learning Visual-Audio Representations for Voice-Controlled Robots2 citations · 2023
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