Influencing Behavioral Attributions to Robot Motion During Task Execution

N. Walker, C. Mavrogiannis, S. Srinivasa, and M. Cakmak, “Influencing Behavioral Attributions to Robot Motion During Task Execution,” Nov. 2021.


Recent literature has proposed algorithms for autonomous generation of robot motion that communicates functional attributes of a robot’s state such as intent or incapability. However, less is known about how to automate the generation of motion for communicating higher-level behavioral attributes such as curiosity or competence. We propose a framework that jointly addresses the challenges of modeling human attributions to robot motion, generating trajectories that elicit attributions, and selecting trajectories that balance attribution and task completion. The insight underpinning our approach is that many attributions can be traced to salient features of the robot’s motion. We illustrate the framework in a coverage task resembling household vacuum cleaning. Through a virtual interface, we collect a dataset of human attributions to robot trajectories during task execution and learn a probabilistic model that maps robot trajectories to human attributions. We then incorporate this model into a trajectory generation mechanism that balances between task completion and communication of a desired behavioral attribution. Through an online user study on a different household layout, we find that our prediction model accurately captures human attribution for coverage tasks.

BibTeX Entry

  author = {Walker, Nick and Mavrogiannis, Christoforos and Srinivasa, Siddhartha and Cakmak, Maya},
  title = {Influencing Behavioral Attributions to Robot Motion During Task Execution},
  booktitle = {Conference on Robot Learning (CoRL)},
  location = {London, UK},
  month = nov,
  year = {2021},
  wwwtype = {conference},
  wwwpdf = {}