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 …

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

Reduction to SAT is a very successful approach to solving hard combinatorial problems in
Artificial Intelligence and computer science in general. Most commonly, problem instances …

The scalability of SAT to very large problems has been achieved in the last ten years, due to
substantially improved encodings, SAT solvers, and algorithms for scheduling the runs of …

We present a new approach to integrated task and motion planning (ITMP) for robots
performing mobile manipulation. In our approach, the user writes a high-level specification …

State-of-the-art planners often exhibit substantial runtime variation, making it useful to be
able to efficiently predict how long a given planner will take to run on a given instance. In …

Planning for real-world applications requires algorithms and tools with the ability to handle
the complexity such scenarios entail. However, meeting the needs of such applications …

The International Planning Competition (IPC) is a prominent event of the artificial
intelligence planning community that has been organized since 1998; it aims at fostering the …

Robots that use natural language in collaborative tasks must refer to objects in their
environment. Recent work has shown the utility of the linguistic theory of the Givenness …

In recent years the field of Automated Planning has significantly advanced and several
powerful domain-independent planners have been developed. However, none of these …