  Manipulation and locomotion are closely related problems that are often
studied in isolation. In this work, we study the problem of coordinating
multiple mobile agents to exhibit manipulation behaviors using a reinforcement
learning (RL) approach. Our method hinges on the use of hierarchical sim2real
-- a simulated environment is used to learn low-level goal-reaching skills,
which are then used as the action space for a high-level RL controller, also
trained in simulation. The full hierarchical policy is then transferred to the
real world in a zero-shot fashion. The application of domain randomization
during training enables the learned behaviors to generalize to real-world
settings, while the use of hierarchy provides a modular paradigm for learning
and transferring increasingly complex behaviors. We evaluate our method on a
number of real-world tasks, including coordinated object manipulation in a
multi-agent setting. See videos at
https://sites.google.com/view/manipulation-via-locomotion

Robotics;Artificial Intelligence;Machine Learning