Learning Whole-Body Manipulation for Quadrupedal Robot

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
$\text{m}$
and 5
$^\circ$
. Real-world experiments demonstrate the successful manipulation of objects such as a 19.2
$\text{kg}$
water-filled drum and a 15.3
$\text{kg}$
plastic box filled with heavy objects while the robot weighs 27
$\text{kg}$
. 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.