Social Mapping of Human-Populated Environments by Implicit Function Learning

International audienceWith robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that can model context-dependent human spatial interactions, encoded in the form of a social map. The core idea of our approach resides in modelling human personal spaces as non-linearly scaled probability functions within the robotic state space and devise the structure and shape of a social map by solving a learning problem in kernel space. The social borders are subsequently obtained as isocontours of the learned implicit function that can realistically model arbitrarily complex social interactions of varying shape and size. We present our experiments using a rich dataset of human interactions, demonstrating the feasibility and utility of the proposed approach and promoting its application to social mapping of human-populated environments

Social Mapping of Human-Populated Environments by Implicit Function Learning

International audienceWith robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that can model context-dependent human spatial interactions, encoded in the form of a social map. The core idea of our approach resides in modelling human personal spaces as non-linearly scaled probability functions within the robotic state space and devise the structure and shape of a social map by solving a learning problem in kernel space. The social borders are subsequently obtained as isocontours of the learned implicit function that can realistically model arbitrarily complex social interactions of varying shape and size. We present our experiments using a rich dataset of human interactions, demonstrating the feasibility and utility of the proposed approach and promoting its application to social mapping of human-populated environments

Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances

Traditionally robots are mostly known by society due to the wide use of manipulators, which are generally placed in controlled environments such as factories. However, with the advances in the area of mobile robotics, they are increasingly inserted into social contexts, i.e., in the presence of people. The adoption of socially acceptable behaviours demands a trade-off between social comfort and other metrics of efficiency. For navigation tasks, for example, humans must be differentiated from other ordinary objects in the scene. In this work, we propose a novel human-aware navigation strategy built upon the use of an adaptive spatial density function that efficiently cluster groups of people according to their spatial arrangement. Space affordances are also used for defining potential activity spaces considering the objects in the scene. The proposed function defines regions where navigation is either discouraged or forbidden. To implement a socially acceptable navigation, the navigation architecture combines a probabilistic roadmap and rapidly-exploring random tree path planners, and an adaptation of the elastic band algorithm. Trials in real and simulated environments carried out demonstrate that the use of the clustering algorithm and social rules in the navigation architecture do not hinder the navigation performance

Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Serious Games in Cultural Heritage

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Knowledge Exchange, Matching, and Agglomeration

Despite wide recognition of their significant role in explaining sustained growth and economic development, uncompensated knowledge spillovers have not yet been fully modeled with a microeconomic foundation. This paper illustrates the exchange of knowledge as well as its consequences for agglomerative activity in a general-equilibrium search-theoretic framework. Agents, possessing differentiated types of knowledge, search for partners to exchange ideas in order to improve production efficacy. Contrary to previous work, we demonstrate that a decentralized equilibrium may be under-populated or over-populated and under-selective or over-selective in knowledge exchange, compared to the social optimum.matching, knowledge exchange and spillovers, agglomerative activity

Learning State-Space Models for Mapping Spatial Motion Patterns

Mapping the surrounding environment is essential for the successful operation of autonomous robots. While extensive research has focused on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Incorporating information about spatial motion patterns can allow mobile robots to navigate and operate successfully in populated areas. In this paper, we propose a deep state-space model that learns the map representations of spatial motion patterns and how they change over time at a certain place. To evaluate our methods, we use two different datasets: one generated dataset with specific motion patterns and another with real-world pedestrian data. We test the performance of our model by evaluating its learning ability, mapping quality, and application to downstream tasks. The results demonstrate that our model can effectively learn the corresponding motion pattern, and has the potential to be applied to robotic application tasks.Comment: 6 pages, 5 figures, to be published in ECMR 2023 conference proceeding

From techno-scientific grammar to organizational syntax. New production insights on the nature of the firm

The paper aims at providing the conceptual building blocks of a theory of the firm which addresses its "ontological questions" (existence,boundaries and organization) by placing production at its core. We draw on engineering for a more accurate description of the production process itself, highlighting its inner complexity and potentially chaotic nature, and on computational linguistics for a production-based account of the nature of economic agents and of the mechanisms through which they build ordered production sets. In so doing, we give a "more appropriate" production basis to the crucial issues of how firm's boundaries are set, how its organisational structure is defined, and how it changes over time. In particular, we show how economic agents select some tasks to be performed internally, while leaving some other to external suppliers, on the basis of criteria based on both the different degrees of internal congruence of the tasks to be performed (i.e. the internal environment), and on the outer relationships carried out with other agents (i.e. the external environment)