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About

Dalong Gao is a robotics researcher whose work sits at the critical intersection of human-robot interaction, assistive robotics, and safety engineering. His research has made significant contributions to understanding and mitigating the physical risks that arise when humans and robotic systems share close physical contact. Gao's most influential work examines injury assessment frameworks for robotics, most notably his adaptation of the automotive Head Injury Criterion (HIC) to evaluate collision risks in robotic systems — a paper that has garnered 73 citations and helped establish a rigorous safety vocabulary for the field. Complementing this, his development of stable, intuitive control strategies for intelligent assist devices (61 citations) and his design of a human-assistive robot capable of handling large payloads (59 citations) demonstrate a rare ability to bridge theoretical safety concerns with practical engineering solutions. A recurring theme in Gao's work is the challenge of haptic stability — specifically, how a human operator's natural tendency to stiffen their arm can paradoxically destabilize robotic interfaces. His research measuring human arm stiffness to improve haptic control (43 citations) represents a particularly elegant contribution to this problem. Across more than a decade of publication, Gao's cumulative work has meaningfully advanced the design of robots that are both powerful and genuinely safe to work alongside.