TL;DR – In this paper, we introduce the idea of connecting FOONs to robotic task and motion planning. We automatically transform a FOON graph, which exists at the object level (i.e., it is a representation that uses meaningful labels or expressions close to human language), into task planning specifications written in PDDL (not a very intuitive way to communicate about tasks).
TL;DR – In this paper, we introduce the idea of connecting FOONs to robotic task and motion planning. We automatically transform a FOON graph, which exists at the object level (i.e., it is a representation that uses meaningful labels or expressions close to human language), into task planning specifications written in PDDL (not a very intuitive way to communicate about tasks).
TL;DR – This was a collaboration with Clemson University’s Yunyi Jia and Yi Chen, who were interested in using FOONs for representing assembly tasks. They successfully utilized and adapted a FOON to robotic assembly execution.
TL;DR – In this paper, we attempt to execute task plan sequences extracted from FOONs. However, these sequences may contain actions that are not executable by a robot. Therefore, a human is introduced in the planning and execution loop, and both the robot and human assistant work together to solve the task.
TL;DR – This work leverages functional object-oriented networks and deep learning for video understanding. In addition, with the deep network framework, we jointly recognize object and action types, which can then be used for constructing new FOON structures.
TL;DR – In this paper, we explore methods in natural language processing (NLP) – specifically semantic similarity – for expanding or generalizing knowledge contained in a FOON. This alleviates the need for demonstrating and annotating graphs by other means.
TL;DR – This was the very first paper on FOON: the functional object-oriented network. Here, we introduced what they are and how they can be used for task planning. They are advantageous for their flexibility and human interpretability.