  Unsupervised skill discovery is a learning paradigm that aims to acquire
diverse behaviors without explicit rewards. However, it faces challenges in
learning complex behaviors and often leads to learning unsafe or undesirable
behaviors. For instance, in various continuous control tasks, current
unsupervised skill discovery methods succeed in learning basic locomotions like
standing but struggle with learning more complex movements such as walking and
running. Moreover, they may acquire unsafe behaviors like tripping and rolling
or navigate to undesirable locations such as pitfalls or hazardous areas. In
response, we present DoDont (Do's and Don'ts), an instruction-based skill
discovery algorithm composed of two stages. First, in an instruction learning
stage, DoDont leverages action-free instruction videos to train an instruction
network to distinguish desirable transitions from undesirable ones. Then, in
the skill learning stage, the instruction network adjusts the reward function
of the skill discovery algorithm to weight the desired behaviors. Specifically,
we integrate the instruction network into a distance-maximizing skill discovery
algorithm, where the instruction network serves as the distance function.
Empirically, with less than 8 instruction videos, DoDont effectively learns
desirable behaviors and avoids undesirable ones across complex continuous
control tasks. Code and videos are available at
https://mynsng.github.io/dodont/
