  Humans leverage multiple sensor modalities when interacting with objects and
discovering their intrinsic properties. Using the visual modality alone is
insufficient for deriving intuition behind object properties (e.g., which of
two boxes is heavier), making it essential to consider non-visual modalities as
well, such as the tactile and auditory. Whereas robots may leverage various
modalities to obtain object property understanding via learned exploratory
interactions with objects (e.g., grasping, lifting, and shaking behaviors),
challenges remain: the implicit knowledge acquired by one robot via object
exploration cannot be directly leveraged by another robot with different
morphology, because the sensor models, observed data distributions, and
interaction capabilities are different across these different robot
configurations. To avoid the costly process of learning interactive object
perception tasks from scratch, we propose a multi-stage projection framework
for each new robot for transferring implicit knowledge of object properties
across heterogeneous robot morphologies. We evaluate our approach on the
object-property recognition and object-identity recognition tasks, using a
dataset containing two heterogeneous robots that perform 7,600 object
interactions. Results indicate that knowledge can be transferred across robots,
such that a newly-deployed robot can bootstrap its recognition models without
exhaustively exploring all objects. We also propose a data augmentation
technique and show that this technique improves the generalization of models.
We release our code and datasets, here:
https://github.com/gtatiya/Implicit-Knowledge-Transfer.
