  The expanding applications of legged robots require their mastery of
versatile motion skills. Correspondingly, researchers must address the
challenge of integrating multiple diverse motion skills into controllers. While
existing reinforcement learning (RL)-based approaches have achieved notable
success in multi-skill integration for legged robots, these methods often
require intricate reward engineering or are restricted to integrating a
predefined set of motion skills constrained by specific task objectives,
resulting in limited flexibility. In this work, we introduce a flexible
multi-skill integration framework named Controllable Skills Integration (CSI).
CSI enables the integration of a diverse set of motion skills with varying
styles into a single policy without the need for complex reward tuning.
Furthermore, in a hierarchical control manner, the trained low-level policy can
be coupled with a high-level Natural Language Inference (NLI) module to enable
preliminary language-directed skill control. Our experiments demonstrate that
CSI can flexibly integrate a diverse array of motion skills more
comprehensively and facilitate the transitions between different skills.
Additionally, CSI exhibits good scalability as the number of motion skills to
be integrated increases significantly.
