  We study the problem of learning physical object representations for robot
manipulation. Understanding object physics is critical for successful object
manipulation, but also challenging because physical object properties can
rarely be inferred from the object's static appearance. In this paper, we
propose DensePhysNet, a system that actively executes a sequence of dynamic
interactions (e.g., sliding and colliding), and uses a deep predictive model
over its visual observations to learn dense, pixel-wise representations that
reflect the physical properties of observed objects. Our experiments in both
simulation and real settings demonstrate that the learned representations carry
rich physical information, and can directly be used to decode physical object
properties such as friction and mass. The use of dense representation enables
DensePhysNet to generalize well to novel scenes with more objects than in
training. With knowledge of object physics, the learned representation also
leads to more accurate and efficient manipulation in downstream tasks than the
state-of-the-art.
