  We tackle the challenge of robotic bin packing with irregular objects, such
as groceries. Given the diverse physical attributes of these objects and the
complex constraints governing their placement and manipulation, employing
preprogrammed strategies becomes unfeasible. Our approach is to learn directly
from expert demonstrations in order to extract implicit task knowledge and
strategies to ensure safe object positioning, efficient use of space, and the
generation of human-like behaviors that enhance human-robot trust.
  We rely on human demonstrations to learn a Markov chain for predicting the
object packing sequence for a given set of items and then compare it with human
performance. Our experimental results show that the model outperforms human
performance by generating sequence predictions that humans classify as
human-like more frequently than human-generated sequences.
  The human demonstrations were collected using our proposed VR platform,
BoxED, which is a box packaging environment for simulating real-world objects
and scenarios for fast and streamlined data collection with the purpose of
teaching robots. We collected data from 43 participants packing a total of 263
boxes with supermarket-like objects, yielding 4644 object manipulations. Our VR
platform can be easily adapted to new scenarios and objects, and is publicly
available, alongside our dataset, at https://github.com/andrejfsantos4/BoxED.
