In Human-Robot Interaction (HRI), ensuring nonambiguous communication between the robot and the human is a key point for carrying out fluently a collaborative task. With this work, we propose a method which allows the robot to generate the optimal set of assertions that are necessary in order to produce an unambiguous reference. In this paper, we present a novel approach to the Referring Expression Generation (REG) problem and its integration into a robotic system. Our method is a domain-independent approach based on an ontology as a knowledge base. We show how this generation can be performed on an ontology which is not dedicated to this task. We then validate our method through simulated situations, compare it with state of the art approach and on a real robotic system.
This paper addresses the problem of taking into account the feasibility and cost of verbal communication actions at the task planning level in the context of human-robot interaction. By determining the human and robot context in which the referring expressions to entities can be effectively achieved and at which cost, our planner can 1) prevent potential deadlocked situations where it is not possible to produce a feasible communication act 2) find plans reducing the overall communication complexity 3) evaluate different communications strategies and select the most suitable one. Our approach is based on the extension of a multi agent hierarchical task planner capable of maintaining, throughout the planning process, an estimate of the human partner knowledge about the environment using a semantic representation. A Referring Expression Generation (REG) can then be executed for each communication action allowing the task planner to be informed about its feasibility and cost during the plan elaboration process. Three scenarios are presented to validate the method, where a robot drives the human actions to perform a cube arrangement task.
The variety and complexity of tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots collaborating with humans. Indeed, humans are either requested for punctual help or are given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach, named Human-Aware Task Planner with Emulation of Human Decisions and Actions (HATP/EHDA), is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human). We present the rationale of our approach along with a formalization and show its potential on an illustrative example.
Assessing robotic architecture for Human-Robot Interaction can be challenging due to the number of features a robot has to endow to perform an acceptable interaction. While everyday-inspired tasks are interesting as reflecting a realistic use of such robots, they often contain a lot of unknown and uncontrolled conditions and specific robot behavior can be hard to test. In this paper, we propose a new psychology-inspired task, gathering perspective-taking, planning, knowledge representation with theory of mind, manipulation, and communication. Along with a precise description of the task allowing its replication, we present a cognitive robot architecture able to perform it in its nominal cases. We finally suggest some challenges and evaluations for the Human-Robot Interaction research community, all derived from this easy-to-replicate task.
The complexity of the tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots interacting with humans to perform tasks. Indeed, humans are either requested for punctual help or given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human(s)). We present the rationale of our approach along with a formalization and show its potential on an illustrative example involving the assembly of a table by a robot and a human.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.