Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …

There is a growing interest in applying pre-trained large language models (LLMs) to
planning problems. However, methods that use LLMs directly as planners are currently …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

The research objectives are to determine: 1). Planning, 2) Organizing, 3) Implementation
and 4) Supervising school principals in learning policies during the Covid-19 pandemic …

Combined Task and Motion Planning (TAMP) is an area where no one-fits-all solution can
exist. Many aspects of the domain, as well as operational requirements, have an effect on …

Robotic planning problems in hybrid state and action spaces can be solved by integrated
task and motion planners (TAMP) that handle the complex interaction between motion-level …

It is essential for users to understand what their AI systems can and can't do in order to use
them safely. However, the problem of enabling users to assess AI systems with sequential …

Designing a planning domain is a difficult task in AI planning. Assisting tools are thus
required if we want planning to be used more broadly. In this paper, we are interested in …

This article aims to describe the planning, implementation, and evaluation of IRE (Islamic
Religious Education) and Character learning at SMK Thoriqul Ulum Pacet. This article uses …

In robotic domains, learning and planning are complicated by continuous state spaces,
continuous action spaces, and long task horizons. In this work, we address these challenges …