Task planning can require defining myriad domain knowledge about the world in which a
robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to …

Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …

Task planning can require defining myriad domain knowledge about the world in which a
robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to …

Generating low-level robot task plans from high-level natural language instructions remains
a challenging problem. Although large language models have shown promising results in …

This paper proposes an approach to automate chemistry experiments using robots by
translating natural language instructions into robot-executable plans, using large language …

Temporal planning methods usually focus on the objective of minimizing makespan.
Unfortunately, this misses a large class of planning problems where it is important to …

We examine the problem of learning models that characterize the high-level behavior of a
system based on observation traces. Our aim is to develop models that are human …

Explainable AI is an important area of research within which Explainable Planning is an
emerging topic. In this paper, we argue that Explainable Planning can be designed as a …

LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic
(LTL) specification interpreted over infinite traces. In this paper we examine the problem of …

Advancements in large language models (LLMs) have demonstrated their potential in
facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs …