LTL-Transfer: Skill Transfer for Temporal Task Specification

TL;DR

LTL-Transfer enables robots to generalize to new tasks specified by linear temporal logic without additional training, by composing learned skills to ensure safety and efficiency in diverse environments.

Contribution

The paper introduces LTL-Transfer, a zero-shot transfer algorithm that composes task-agnostic skills for novel LTL task specifications, reducing training data requirements.

Findings

Achieves over 90% success on unseen tasks after training on 50 tasks.

Solves 100% of 300 novel tasks without safety violations.

Demonstrated effective transfer on a quadruped robot for fetch-and-deliver and navigation tasks.

Abstract

Deploying robots in real-world environments, such as households and manufacturing lines, requires generalization across novel task specifications without violating safety constraints. Linear temporal logic (LTL) is a widely used task specification language with a compositional grammar that naturally induces commonalities among tasks while preserving safety guarantees. However, most prior work on reinforcement learning with LTL specifications treats every new task independently, thus requiring large amounts of training data to generalize. We propose LTL-Transfer, a zero-shot transfer algorithm that composes task-agnostic skills learned during training to safely satisfy a wide variety of novel LTL task specifications. Experiments in Minecraft-inspired domains show that after training on only 50 tasks, LTL-Transfer can solve over 90% of 100 challenging unseen tasks and 100% of 300 commonly used novel tasks without violating any safety constraints. We deployed LTL-Transfer at the task-planning level of a quadruped mobile manipulator to demonstrate its zero-shot transfer ability for fetch-and-deliver and navigation tasks.