A Generic Multi-Purpose Robot

Robotics and Autonomous …, 2007

Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. The work presented here is oriented in this direction. It suggests a hierarchical probabilistic representation of space that is based on objects. A global topological representation of places with object graphs serving as local maps is proposed. The work also details the first efforts towards conceptualizing space on the basis of the human compatible representation so formed. Such a representation and the resulting conceptualization would be useful for enabling robots to be cognizant of their surroundings. Experiments on place classification and place recognition are reported in order to demonstrate the applicability of such a representation towards understanding space and thereby performing spatial cognition. Further, relevant results from user studies validating the proposed representation are also reported. Thus, the theme of the work is -representation for spatial cognition.

Proceedings of the Combined Workshop on Spatial Language Understanding (, 2019

It is important, for human-robot interaction, to endow the robot with the knowledge necessary to understand human needs and to be able to respond to them. We present a formalized and unified representation for indoor environments using an ontology devised for a route description task in which a robot must provide explanations to a person. We show that this representation can be used to choose a route to explain to a human as well as to verbalize it using a route perspective. Based on ontology, this representation has a strong possibility of evolution to adapt to many other applications. With it, we get the semantics of the environment elements while keeping a description of the known connectivity of the environment. This representation and the illustration algorithms, to find and verbalize a route, have been tested in two environments of different scales.

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007

The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim of making robots more spatially cognizant, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts from the perceived objects and attempts to classify places using the concepts gleaned. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is -conceptualization and classification for representation and spatial cognition.

2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2018

A robot, in order to be autonomous, needs some kind of representation of its surrounding environment. From a general point of view, basic robotic tasks (such as localization, mapping, object handling, etc.) can be carried out with only very simple geometric primitives, usually extracted from raw sensor data. But whenever an interaction with a human being is involved, robots must have an understanding of concepts expressed in human natural language. In most approaches, this is done through a prebuilt ontology. In this paper, we try to bridge the gap between data driven methods and semantic based approaches by introducing a 3layer environment model based on "instances" : sensor data based observations of concepts stored in a knowledge graph. We will focus on our original object-oriented ontology construction and illustrate the flow of our model in a simple showcase.

2014

Robotics in the 21st century will progress from scripted interactions with the physical world, where human programming input is the bottleneck in the robot's ability to sense, think and act, to a point where the robotic system is able to autonomously generate adaptive representations of its surroundings, and further, to implement decisions regarding this environment. A key factor in this development will be the ability of the robotic platform to understand its physical space. In this paper, we describe a rationale and framework for developing spatial understanding in a robotics platform, using knowledge representation in the form of a hybrid spatial- ontological model of the physical world. Further, we describe the proposed CogOnto (cognitive ontology) model, which enables symbol grounding for a cognitive computing system, using sensor data gathered from diverse and heterogeneous sources, associated with humanly crafted symbolic descriptors. While such a system may be implemente...

2006 IEEE/RSJ …, 2006

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, 2011

Robotic systems are today capable of performing patrolling and surveillance tasks in indoor structured environments. However, they need to be designed by taking into account the operational environment and the specific task to be accomplished. This dependency from the specific features of task and environment (contextual information, according to Turner ), severely restricts their practical deployment. In this paper, we address the creation of semantic maps for the effective deployment of surveillance robot in an a priori unknown indoor scenario. More specifically, we propose an architecture based on multimodal human-robot interaction that allows the user to specify in a natural way, knowledge about the environment that is represented in a semantic map. Given the acquired knowledge, the robot is then capable to plan and execute an effective patrolling of the scenario.

2003

The system we describe in this paper is an indoor intelligent system, that is still under development. Our goal is to evolve the system with abilities suitable for a domestic robot, that has to cope with a continuously evolving ambient, the house. The interest of the system relies in its architecture that has been studied and designed for the purpose of integrating some specific cognitive behavior like perception, symbolic map construction and making decisions in a whole hierarchy of intercommunicating levels.

Spatial cognition III, 2003

2020

This article deals with the problem of room categorization, i.e. the classification of a room as being a bathroom, kitchen, living-room, bedroom, etc., by an autonomous robot operating in home environments. For that, we propose a room categorization system based on a Bayesian probabilistic framework that combines object detections and its semantics. For detecting objects we resort to a state-of-theart CNN, Mask R-CNN, while the meaning or semantics of those detections is provided by an ontology. Such an ontology encodes the relations between object and room categories, that is, in which room types the different object categories are typically found (toilets in bathrooms, microwaves in kitchens, etc.). The Bayesian framework is in charge of fusing both sources of information and providing a probability distribution over the set of categories the room can belong to. The proposed system has been evaluated in houses from the Robot@Home dataset, validating its effectiveness under real-world conditions. CCS CONCEPTS • Computing methodologies → Cognitive robotics; Probabilistic reasoning; Ontology engineering.