Evaluating Recipes Generated from Functional Object-Oriented Network

• URL: http://arxiv.org/abs/2106.00728v1
• Date: Tue, 1 Jun 2021 19:00:52 GMT
• Title: Evaluating Recipes Generated from Functional Object-Oriented Network
• Authors: Md Sadman Sakib, Hailey Baez, David Paulius, and Yu Sun
• Abstract summary: The functional object-oriented network (FOON) has been introduced as a knowledge representation, which takes the form of a graph. To get a sequential plan for a manipulation task, a robot can obtain a task tree through a knowledge retrieval process from the FOON. We compare the quality of an acquired task tree with a conventional form of task knowledge, such as recipes or manuals.
• Score: 4.94338660039249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The functional object-oriented network (FOON) has been introduced as a knowledge representation, which takes the form of a graph, for symbolic task planning. To get a sequential plan for a manipulation task, a robot can obtain a task tree through a knowledge retrieval process from the FOON. To evaluate the quality of an acquired task tree, we compare it with a conventional form of task knowledge, such as recipes or manuals. We first automatically convert task trees to recipes, and we then compare them with the human-created recipes in the Recipe1M+ dataset via a survey. Our preliminary study finds no significant difference between the recipes in Recipe1M+ and the recipes generated from FOON task trees in terms of correctness, completeness, and clarity.

Related papers

• Task Tree Retrieval For Robotic Cooking [0.0]
  This paper is based on developing different algorithms, which generate the task tree planning for the given goal node(recipe) The knowledge representation of the dishes is called FOON. It contains the different objects and their between them with respective to the motion node.
  arXiv  Detail & Related papers  (2023-11-27T20:41:21Z)
• Extracting task trees using knowledge retrieval search algorithms in functional object-oriented network [0.0]
  The functional object-oriented network (FOON) has been developed as a knowledge representation method that can be used by robots. A FOON can be observed as a graph that can provide an ordered plan for robots to retrieve a task tree.
  arXiv  Detail & Related papers  (2022-11-15T17:20:08Z)
• Knowledge Retrieval for Robotic Cooking [0.0]
  The motivation behind developing search algorithms in Functional Object-Oriented Networks is that most of the time, a certain recipe needs to be retrieved or ingredients for a certain recipe needs to be determined. This paper shows several proposed weighted FOON and task planning algorithms that allow a robot and a human to successfully complete complicated tasks together.
  arXiv  Detail & Related papers  (2022-11-08T19:40:27Z)
• KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Few-Shot NLP [68.43279384561352]
  Existing data augmentation algorithms leverage task-independent rules or fine-tune general-purpose pre-trained language models. These methods have trivial task-specific knowledge and are limited to yielding low-quality synthetic data for weak baselines in simple tasks. We propose the Knowledge Mixture Data Augmentation Model (KnowDA): an encoder-decoder LM pretrained on a mixture of diverse NLP tasks.
  arXiv  Detail & Related papers  (2022-06-21T11:34:02Z)
• Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization [101.72755769194677]
  We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph. Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:44:25Z)
• Active Multi-Task Representation Learning [50.13453053304159]
  We give the first formal study on resource task sampling by leveraging the techniques from active learning. We propose an algorithm that iteratively estimates the relevance of each source task to the target task and samples from each source task based on the estimated relevance.
  arXiv  Detail & Related papers  (2022-02-02T08:23:24Z)
• Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems [5.400294730456784]
  Unlike humans, robots cannot creatively adapt to novel scenarios. Existing knowledge in the form of a knowledge graph is used as a base of reference to create task trees. Our results indicate that the proposed method can produce task plans with high accuracy even for never-before-seen ingredient combinations.
  arXiv  Detail & Related papers  (2021-12-04T21:28:22Z)
• Learning Structural Representations for Recipe Generation and Food Retrieval [101.97397967958722]
  We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset.
  arXiv  Detail & Related papers  (2021-10-04T06:36:31Z)
• Structure-Aware Generation Network for Recipe Generation from Images [142.047662926209]
  We investigate an open research task of generating cooking instructions based on only food images and ingredients. Target recipes are long-length paragraphs and do not have annotations on structure information. We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task.
  arXiv  Detail & Related papers  (2020-09-02T10:54:25Z)
• Multi-modal Cooking Workflow Construction for Food Recipes [147.4435186953995]
  We build MM-ReS, the first large-scale dataset for cooking workflow construction. We propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow.
  arXiv  Detail & Related papers  (2020-08-20T18:31:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.