The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

We present the first approach capable of learning domain-independent planning heuristics
entirely from scratch. The heuristics we learn map the hypergraph representation of the …

Graph learning is naturally well suited for use in symbolic, object-centric planning due to its
ability to exploit relational structures exhibited in planning domains and to take as input …

In this paper, we propose a deep neural network that predicts the feasibility of a mixed-
integer program from visual input for robot manipulation planning. Integrating learning into …

Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …

Robotic motion planning problems are typically solved by constructing a search tree of valid
maneuvers from a start to a goal configuration. Limited onboard computation and real-time …

In this paper, we propose a deep convolutional recurrent neural network that predicts action
sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP …

In imitation learning for planning, parameters of heuristic functions are optimized against a
set of solved problem instances. This work revisits the necessary and sufficient conditions of …

Current approaches for learning for planning have yet to achieve competitive performance
against classical planners in several domains, and have poor overall performance. In this …

A hallmark of intelligence is the ability to deduce general principles from examples, which
are correct beyond the range of those observed. Generalized Planning deals with finding …