  Despite many successful applications of data-driven control in robotics,
extracting meaningful diverse behaviors remains a challenge. Typically, task
performance needs to be compromised in order to achieve diversity. In many
scenarios, task requirements are specified as a multitude of reward terms, each
requiring a different trade-off. In this work, we take a constrained
optimization viewpoint on the quality-diversity trade-off and show that we can
obtain diverse policies while imposing constraints on their value functions
which are defined through distinct rewards. In line with previous work, further
control of the diversity level can be achieved through an attract-repel reward
term motivated by the Van der Waals force. We demonstrate the effectiveness of
our method on a local navigation task where a quadruped robot needs to reach
the target within a finite horizon. Finally, our trained policies transfer well
to the real 12-DoF quadruped robot, Solo12, and exhibit diverse agile behaviors
with successful obstacle traversal.
