In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

Human-robot collaboration plays an important role in intelligent manufacturing. However,
the main challenge is how the robot can make online reactive changes to the plan based on …

Temporal logic-based motion planning has been extensively studied to address complex
robotic tasks. However, existing works primarily focus on static environments or assume the …

We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints.
The language of LTL allows flexible description of tasks that may be unnatural to encode as …

It is an interesting open problem to enable robots to efficiently and effectively learn long-
horizon manipulation skills. Motivated to augment robot learning via more effective …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of control problems. However, applications in safety-critical …

This work develops a fast task allocation framework for heterogeneous multi-robot systems
subject to both temporal logic and inter-task constraints. The considered inter-task …

Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization
that may otherwise be difficult to describe with scalar reward functions. However, the …