Efficient Data Collection for Robotic Manipulation via Compositional
  Generalization
Jensen Gao Annie Xie Ted Xiao Chelsea Finn Dorsa Sadigh
Data collection has become an increasingly important problem in robotic
manipulation, yet there still lacks much understanding of how to effectively
collect data to facilitate broad generalization. Recent works on large-scale
robotic data collection typically vary a wide range of environmental factors
during data collection, such as object types and table textures. While these
works attempt to cover a diverse variety of scenarios, they do not explicitly
account for the possible compositional abilities of policies trained on the
data. If robot policies are able to compose different environmental factors of
variation (e.g., object types, table heights) from their training data to
succeed when encountering unseen factor combinations, then we can exploit this
to avoid collecting data for situations that composition would address. To
investigate this possibility, we conduct thorough empirical studies both in
simulation and on a real robot that compare data collection strategies and
assess whether visual imitation learning policies can compose environmental
factors. We find that policies do exhibit composition, although leveraging
prior robotic datasets is critical for this on a real robot. We use these
insights to provide better practices for in-domain data collection by proposing
data collection strategies that exploit composition, which can induce better
generalization than naive approaches for the same amount of effort during data
collection. We further demonstrate that a real robot policy trained on data
from such a strategy achieves a success rate of 77.5% when transferred to
entirely new environments that encompass unseen combinations of environmental
factors, whereas policies trained using data collected without accounting for
environmental variation fail to transfer effectively, with a success rate of
only 2.5%. We provide videos at http://iliad.stanford.edu/robot-data-comp/.