We present a new open-source software framework (called Humanoid Path Planner – HPP) for path and manipulation planning. Some features like built-in kinematic chains and implementation of non-linear constraints make the framework a good fit for humanoid robot applications. The implementation of kinematic chains takes into account the Lie-group structure (rotation in 3D space SO(3)) of robot con...

We present a new corpus, PersonaBank, consisting of 108 personal stories from weblogs that have been annotated with their STORY INTENTION GRAPHS, a deep representation of the fabula of a story. We describe the topics of the stories and the basis of the STORY INTENTION GRAPH representation, as well as the process of annotating the stories to produce the STORY INTENTION GRAPHs and the challenges ...

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search t...

Like the previous version of LPG, the new version is based on a stochastic local search in the space of particular “action graphs” derived from the planning problem specification. In LPG-TD, this graph representation has been extended to deal with the new features of PDDL2.2, as well to improve the management of durative actions and of numerical expressions (already supported by PDDL2.1 (Fox & ...

An increasing number of planners can handle uncertainty in the domain or in action outcomes. However, less work has addressed building plans when the planner’s world can change independently of the planning agent in an uncertain manner. In this paper, I model this change with external events that concisely represent some aspects of structure in the planner’s domain. This event model is given a ...

Learning from experience can significantly improve the performance of search based planners, especially for challenging problems like high-dimensional planning. Experience Graph (E-Graph) is a recently developed framework that encodes experiences, obtained from solving instances in the past, into a single bounded-admissible heuristic, and uses it to guide the search. While the E-Graph approach ...