ACM/IEEE International Conference on Human-Robot Interaction Test of Time Award
The HRI Test of Time (ToT) Award is an annual award established in 2026 to recognize papers published at the ACM/IEEE Conference on Human-Robot Interaction (HRI) that have demonstrated a lasting and substantial impact on the HRI community and beyond. The award aims to honour seminal contributions that have shaped the field, introduced new ways of thinking or working, and continue to influence research, practice, and industry over time. Eligible papers must be at least 10 years old at the time the award is given.
The selection process is conducted by the HRI Award Subcommittee, which is formed by the HRI Steering Committee. The awards are presented during the annual ACM/IEEE Conference on Human-Robot Interaction (HRI).
Nominations are accepted year-round and are open to all members of the community. Nominations received by November 30 will be considered for the awards presented at the following year’s edition of the conference. Each nomination remains active for three years.
Nominations for 2027 can be submitted via a short Google Form: https://forms.gle/wA8xxqN38BR781W56
2026 Award Winners
Aaron Steinfeld, Terrence Fong, David Kaber, Michael Lewis, Jean Scholtz, Alan Schultz, Michael Goodrich
Common Metrics for Human-Robot Interaction
(1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, 2006)
Nominators’ comments:
“This paper is foundational in establishing the earliest methods and metrics for studying, evaluating, and exploring human–robot interaction. […] It continues to serve as a landmark reference and a guiding framework for HRI researchers today.”
“While there have been many great experiments, design insights, discoveries, and technical innovations, those papers typically impact a single part of the field. This paper impacted the whole field.”
Anca D. Dragan, Kenton C.T. Lee, Siddhartha S. Srinivasa
Legibility and Predictability of Robot Motion
(8th ACM/IEEE International Conference on Human-Robot Interaction, 2013)
Nominators’ comments:
“This paper has proven to be a watershed moment in the history of Human-Robot Interaction. It fundamentally shifted the community’s perspective on robot motion generation, establishing theoretical frameworks and algorithmic approaches that remain the standard in the field over a decade later. […] [It] transformed robot behavior generation from a functional optimization problem into a communicative, human-centric algorithmic challenge. It is the definition of a seminal work.”