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 …

The SAT Competitions constitute a well-established series of yearly open international
algorithm implementation competitions, focusing on the Boolean satisfiability (or …

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

Task and motion planning (TMP) combines discrete search and continuous motion planning.
Earlier work has shown that to efficiently find a task-motion plan, the discrete search can …

This paper presents FAMA, a novel approach for learning Strips action models from
observations of plan executions that compiles the learning task into a classical planning …

One of the oldest and most popular approaches to automated planning is to encode the
problem at hand into a propositional formula and use a Satisfiability (SAT) solver to find a …

Creating a domain model, even for classical, domain-independent planning, is a notoriously
hard knowledge-engineering task. A natural approach to solve this problem is to learn a …

This paper presents a novel approach for learning strips action models from examples that
compiles this inductive learning task into a classical planning task. Interestingly, the …

Both search-based and translation-based planning systems usually operate on grounded
representations of the problem. Planning models, however, are commonly defined using …

Many manipulation tasks combine high-level discrete planning over actions with low-level
motion planning over continuous robot motions. Task and motion planning (TMP) provides a …