  Deploying robots in real-world domains, such as households and flexible
manufacturing lines, requires the robots to be taskable on demand. Linear
temporal logic (LTL) is a widely-used specification language with a
compositional grammar that naturally induces commonalities across tasks.
However, the majority of prior research on reinforcement learning with LTL
specifications treats every new formula independently. We propose LTL-Transfer,
a novel algorithm that enables subpolicy reuse across tasks by segmenting
policies for training tasks into portable transition-centric skills capable of
satisfying a wide array of unseen LTL specifications while respecting
safety-critical constraints. Experiments in a Minecraft-inspired domain show
that LTL-Transfer can satisfy over 90% of 500 unseen tasks after training on
only 50 task specifications and never violating a safety constraint. We also
deployed LTL-Transfer on a quadruped mobile manipulator in an analog household
environment to demonstrate its ability to transfer to many fetch and delivery
tasks in a zero-shot fashion.
