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Found 14 papers, 2 papers with code
CAPE: Corrective Actions from Precondition Errors using Large Language Models
Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76. 49% compared to SayCan.
Long-Horizon Planning and Execution with Functional Object-Oriented Networks
We demonstrate our entire approach on long-horizon tasks in CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.
Grounding of the Functional Object-Oriented Network in Industrial Tasks
Our initial results show that FOON can be used for an industrial use case and that we can use existing linked data models in LfD applications.
Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems
To address the problem of producing novel and flexible task plans called task trees, we explore how we can derive plans with concepts not originally in the robot's knowledge base.
A Road-map to Robot Task Execution with the Functional Object-Oriented Network
Following work on joint object-action representations, the functional object-oriented network (FOON) was introduced as a knowledge graph representation for robots.
Evaluating Recipes Generated from Functional Object-Oriented Network
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.
Developing Motion Code Embedding for Action Recognition in Videos
In this work, we propose a motion embedding strategy known as motion codes, which is a vectorized representation of motions based on a manipulation's salient mechanical attributes.
Estimating Motion Codes from Demonstration Videos
A motion taxonomy can encode manipulations as a binary-encoded representation, which we refer to as motion codes.
A Motion Taxonomy for Manipulation Embedding
To represent motions from a mechanical point of view, this paper explores motion embedding using the motion taxonomy.
Manipulation Motion Taxonomy and Coding for Robots
This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations.
Task Planning with a Weighted Functional Object-Oriented Network
The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort.
Functional Object-Oriented Network: Construction & Expansion
To further improve the performance of knowledge retrieval as a follow up to our previous work, we discuss generalizing knowledge to be applied to objects which are similar to what we have in FOON without manually annotating new sources of knowledge.
A Survey of Knowledge Representation in Service Robotics
Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots.
Long Activity Video Understanding using Functional Object-Oriented Network
Each action is therefore associated with a functional unit and the sequence of actions is further evaluated to identify the single on-going activity in the video.
