LESS is more: Rethinking probabilistic models of human behavior

Andreea Bobu, Dexter R.R. Scobee, Jaime F. Fisac, S. Shankar Sastry, Anca D. Dragan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward. While this model has been successful in a variety of robotics domains, its roots lie in econometrics, and in modeling decisions among different discrete options, each with its own utility or reward. In contrast, human trajectories lie in a continuous space, with continuous-valued features that influence the reward function. We propose that it is time to rethink the Boltzmann model, and design it from the ground up to operate over such trajectory spaces. We introduce a model that explicitly accounts for distances between trajectories, rather than only their rewards. Rather than each trajectory affecting the decision independently, similar trajectories now affect the decision together. We start by showing that our model better explains human behavior in a user study.We then analyze the implications this has for robot inference, first in toy environments where we have ground truth and find more accurate inference, and finally for a 7DOF robot arm learning from user demonstrations.

Original languageEnglish (US)
Title of host publicationHRI 2020 - Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Number of pages9
ISBN (Electronic)9781450367462
StatePublished - Mar 9 2020
Externally publishedYes
Event15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 - Cambridge, United Kingdom
Duration: Mar 23 2020Mar 26 2020

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148


Conference15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering


  • Human decision modeling
  • Robot inference and prediction


Dive into the research topics of 'LESS is more: Rethinking probabilistic models of human behavior'. Together they form a unique fingerprint.

Cite this