  We propose a learning-based system for enabling quadrupedal robots to
manipulate large, heavy objects using their whole body. Our system is based on
a hierarchical control strategy that uses the deep latent variable embedding
which captures manipulation-relevant information from interactions,
proprioception, and action history, allowing the robot to implicitly understand
object properties. We evaluate our framework in both simulation and real-world
scenarios. In the simulation, it achieves a success rate of 93.6 % in
accurately re-positioning and re-orienting various objects within a tolerance
of 0.03 m and 5 {\deg}. Real-world experiments demonstrate the successful
manipulation of objects such as a 19.2 kg water-filled drum and a 15.3 kg
plastic box filled with heavy objects while the robot weighs 27 kg. Unlike
previous works that focus on manipulating small and light objects using
prehensile manipulation, our framework illustrates the possibility of using
quadrupeds for manipulating large and heavy objects that are ungraspable with
the robot's entire body. Our method does not require explicit object modeling
and offers significant computational efficiency compared to optimization-based
methods. The video can be found at https://youtu.be/fO_PVr27QxU.
