With the topics related to the intelligent AUV, control and navigation have become one of the
key researching fields. This paper presents a concise and reliable path planning method for …

Planning is the model-based approach to autonomous behavior where the agent behavior is
derived automatically from a model of the actions, sensors, and goals. The main challenges …

Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the
generic task of trial-and-error learning, and its effectiveness has been convincingly …

It is important for a robot to be able to interpret natural language commands given by a
human. In this paper, we consider performing a sequence of mobile manipulation tasks with …

We present a new algorithm for task and motion planning (TMP) and discuss the
requirements and abstractions necessary to obtain robust solutions for TMP in general. Our …

Generalized planning studies the representation, computation and evaluation of solutions
that are valid for multiple planning instances. These are topics studied since the early days …

Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans,
that is, solutions to planning problems, that transform a given state of the world to another …

We present a new constraint-based framework for task and motion planning (TMP). Our
approach is extensible, probabilistically complete, and offers improved performance and …

To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits
must be compiled to take into account constraints on the hardware. For near-term hardware …

In this paper we introduce a knowledge engine, which learns and shares knowledge
representations, for robots to carry out a variety of tasks. Building such an engine brings with …