Human-populated Environments Research Articles

A Collaborative Homeostatic-Based Behavior Controller for Social Robots in Human–Robot Interaction Experiments

Robots have been gradually leaving laboratory and factory environments and moving into human populated environments. Various social robots have been developed with the ability to exhibit social behaviors and collaborate with non-expert users in different situations. In order to increase the degree of collaboration between humans and the robots in human–robot joint action systems, these robots need to achieve higher levels of interaction with humans. However, many social robots are operated under teleoperation modes or pre-programmed scenarios. Based on homeostatic drive theory, this paper presents the development of a novel collaborative behavior controller for social robots to jointly perform tasks with users in human–robot interaction (HRI) experiments. Manual work during the experiments is reduced, and the experimenters can focus more on the interaction. We propose a hybrid concept for the behavior decision-making process, which combines the hierarchical approach and parallel-rooted, ordered, slip-stack hierarchical architecture. Emotions are associated with behaviors by using the two-dimensional space model of valence and arousal. We validate the usage of the behavior controller by a joint attention HRI scenario in which the NAO robot and a therapist jointly interact with children.

Semantic maps from multiple visual cues

Future service robots targeted to operate in domestic or industrial environment and in close collaboration with humans should possess the ability to produce meaningful internal perceptual representations of their own surroundings, enabling them to fulfill a variety of real-world tasks. For this purpose, we present here a semantic mapping framework featuring geometrical and semasiological attributes that reveal the relationships between objects and places in a real-life environment. The geometrical component consists of a 3D metric map, onto which a topological map is deployed. The semasiological part is realized by putting together a place recognition algorithm and an object recognition one. The categorization of the different places relies on the resolution of appearance-based consistency histograms, while for the recognition of objects in the scene, a hierarchical temporal memory (HTM) network boosted by a saliency attentional model, is utilized. These semantic attributes are then deposited on the topological map to augment it with the belief distributions regarding the visited places, enabling thus the agent to act in an intelligent manner in human populated environments. Thus, the proposed framework outlines a proficient system in the construction of human conceivable environment representations, which has been successfully assessed on real-world scenarios, proving its ability to provide a consistent solution to the emerging problem of the human-robot cohabitation.

Robot navigation in large-scale social maps: An action recognition approach

As robots tend to establish their presence in every day human environments the necessity for them to attain socially acceptable behavior is a condition sine qua non. Consequently, robots need to learn and react appropriately, should they be able to share the same space with people and to reconcile their operation to man’s activity. This work proposes an integrated robot framework that allows navigation in a human populated environment. This is the first work that employs the performed actions of individuals so as to re-plan and design a collision-free and at the same time a socially acceptable trajectory. Expandability is another feature of the suggested mapping module since it is capable of incorporating an unconstrained number of actions and subsequently responses, according to the needs of the task in hand and the environment in which the robot operates. Moreover, the paper addresses the integration of the proposed mapping module with the rest of the robot framework in order to operate in a seamless fashion. The generic design of this architecture allows the replacement of modules with other similar ones, thus providing adaptability with respect to the environment and so on. The method utilizes off-line constructed 3D metric maps organized in terms of a topological graph. During its perambulation the robot is ample to detect humans while it exploits deep learning strategies to recognize their activities. The memorized actions are seamlessly associated with specific rules –deriving from the proxemics theory– and are organized in an efficient manner to be recalled during robot’s navigation. Moreover, the paper exhibits the differences of the robot navigation in inhabited and uninhabited environments and demonstrates the alteration of the robot’s trajectory with respect to the recognized actions and poses of the individuals. The system has been evaluated on a robot able to acquire RGB-D data in domestic environments. The human detection and the action recognition modules exhibited remarkable performance, the human detection one was flawless about its decision while the action recognition one confused actions regarding the number of individuals that participate in them. Last, the robot navigation component was proved capable of extracting safe trajectories in human populated environments.

Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory

Being aware of mutual influences between individuals is a major requirement a robot to efficiently operate in human populated environments. This is especially true for the navigation among humans with its mutual avoidance maneuvers. While humans easily manage this task, robotic systems are still facing problems. Most of the recent approaches concentrate on predicting the motions of humans individually and deciding afterwards. Thereby, interactivity is mostly neglected. In this work, we go one step back and focus on understanding the underlying principle of human decision making in the presence of multiple humans. Non-cooperative game theory is applied to formulate the problem of predicting the decisions of multiple humans that interact which each other during navigation. Therefore, we use the theory of Nash equilibria in static and dynamic games where different cost functions from literature rate the payoffs of the individual humans. The approach anticipates collisions and additionally reasons about several avoidance maneuvers of all humans. For the evaluation of the game theoretic approach we recorded trajectories of humans passing each other. The evaluation shows that game theory is able to reproduce the decision process of humans more accurately than a decision model that predicts humans individually.

Institutional Robotics

The way human beings engage with material things in our environment is experiencing rapid modification. Human and non-human, natural and artificial creatures are on the verge of building unprecedented relations of sociability. This paper takes this process as a horizon for Social Robotics, advancing a new approach to coordinate systems of multiple robots within social spaces durably shared by humans and machines. Given the fact that institutions are the tools in use within human societies to shape social action over long periods of time, we use human-inspired institutions to deal with scenarios involving many-to-many human-robot lasting interactions. Our approach, Institutional Robotics, is inspired by leading economists and philosophers having dedicated sustained efforts to the understanding of social institutions. This paper: (1) advocates the importance of an institution-based approach for multi-robot systems (Institutional Robotics) in real-world human-populated environments, where many-to-many social interactions among robots and humans must be considered; (2) reviews experiments conducted (including novel experimental work) and methodologies used in the process of advancing Institutional Robotics. Both contributions pave the way for a new institution-based methodology to coordinate robot collectives, which stems from an inter-disciplinary approach based on robotics, social sciences and philosophy.

Socially aware path planning for mobile robots

SUMMARYHuman–robot interaction is an emerging area of research where a robot may need to be working in human-populated environments. Human trajectories are generally not random and can belong to gross patterns. Knowledge about these patterns can be learned through observation. In this paper, we address the problem of a robot's social awareness by learning human motion patterns and integrating them in path planning. The gross motion patterns are learned using a novel Sampled Hidden Markov Model, which allows the integration of partial observations in dynamic model building. This model is used in the modified A* path planning algorithm to achieve socially aware trajectories. Novelty of the proposed method is that it can be used on a mobile robot for simultaneous online learning and path planning. The experiments carried out in an office environment show that the paths can be planned seamlessly, avoiding personal spaces of occupants.

Learning intentions for improved human motion prediction

For many tasks robots need to operate in human populated environments. Human motion prediction is gaining importance since this helps minimizing the hinder robots cause during the execution of these tasks. The concept of social forces defines virtual repelling and attracting forces from and to obstacles and points of interest. These social forces can be used to model typical human movements given an environment and a person’s intention. This work shows how such models can exploit typical motion patterns summarized by growing hidden Markov models (GHMMs) that can be learned from data online and without human intervention. An extensive series of experiments shows that exploiting a person’s intended position estimated using a GHMM within a social forces based motion model yields a significant performance gain in comparison with the standard constant velocity-based models.

Données récentes sur les hantavirus et perspectives de recherche

Les virus du genre Hantavirus sont les seuls représentants de la famille des Bunyaviridae qui ne sont pas transmis par des vecteurs arthropodes mais par des petits mammifères. Les hantavirus transmis par les rongeurs (Ordre Rodentia) ont été les premiers découverts en raison de leur pouvoir pathogène chez l’homme. Les premières études phylogénétiques ont suggéré une coévolution entre chaque hantavirus et son espèce de rongeur réservoir. Toutefois, l’exploration d’autres réservoirs animaux a montré que des hantavirus circulent aussi chez les insectivores (Ordre Soricomorpha) et les chauvessouris (Ordre Chiroptera), sans qu’aucune transmission pathogène à l’homme n’ait pu être mise en évidence jusqu’à aujourd’hui. Des observations naturelles de cocirculation d’un même hantavirus au sein de plusieurs espèces de rongeurs sympatriques, ainsi que de nouvelles données phylogénétiques qui soulignent des changements d’hôte (host-switching) entre hantavirus très proches, remettent actuellement en cause la cospéciation stricte. De même, l’observation plus fine de cas cliniques suggère de modérer le dogme d’une maladie distincte chez l’homme, les hantavirus de l’Ancien Monde (Europe-Asie) provoquant une fièvre hémorragique à syndrome rénal (FHRS) et ceux du Nouveau Monde (Amériques) conduisant à une hantavirose à syndrome cardio-pulmonaire (HSCP). Ces points sont discutés car ils ouvrent d’importantes perspectives en matière de recherche transdisciplinaire, depuis l’immunologie comparée chez les mammifères jusqu’à la modélisation de la dynamique des réservoirs dans leur milieu naturel, en passant par la sociologie des populations à risque. Les données récentes sur la circulation et le pouvoir pathogène des hantavirus en Europe et dans le monde sont aussi présentées, ainsi que les nouveaux outils d’investigation sérologique et génétique permettant la découverte sans a priori de nouveaux virus «dormants» dans des réservoirs et l’évaluation de leur potentiel d’émergence chez l’homme.

Measuring Human-Robot Interaction Through Motor Resonance

In the last decades, the introduction of robotic devices in fields such as industries, dangerous environments, and medicine has notably improved working practices. The availability of a new generation of humanoid robots for everyday’s activities in human populated environments can entail an even wider revolution. Indeed, not only domestic activities but also social behaviors will adapt to a continuous interaction with a completely new kind of social agents. In the light of this scenario, it becomes crucial to design robots suited to natural cooperation with humans, and contextually to develop quantitative methods to measure human-robot interaction (HRI). Motor resonance, i.e. the activation of the observer’s motor control system during action perception, has been suggested to be a key component of human social behavior, and as such is thought to play a central role for HRI. In the literature there are reports of robots that have been used as tools to understand the human brain. The aim of this review is to offer a different perspective in suggesting that human responses can become a tool to measure and improve robot interactional attitudes. In the first part of the paper the notion of motor resonance and its neurophysiological correlates are introduced. Subsequently we describe motor resonance studies on the perception of robotic agents’ behavior. Finally we introduce proactive gaze and automatic imitation, two techniques adopted in human motor resonance studies, and we present the advantages which would follow their application to HRI.

Enforcing safety of cyberphysical systems using flatness and abstraction

The diffusion of cyberphysical systems acting in human-populated environments brings to the fore the problem of implementing provably safe control laws, to avoid potentially dangerous collisions between moving parts of the system, and with nearby obstacles, without compromising the system's functionality. The limiting factor in most implementations is the model's complexity, and a common workaround includes the reduction of the physical model, based on differential equations, to a finite symbolic model. Following this strategy, we are investigating ways to exploit the specific structure of many mechanical systems (the differentially flat systems) to achieve this simplification. Our objective is to construct a supervisor enforcing a given set of safety rules, while imposing as little constraints as possible on the system's functionality. In this paper, we outline our approach, and present an example -- a collision avoidance algorithm for a fleet of vehicles converging to an intersection. Our approach improves on previous results by providing a deterministic symbolic model for a class of system, regardless of their stability properties, and by addressing explicitly the problem of safety enforcing.

Imitating Operations On Internal Cognitive Structures for Language Aquisition

Imitating Operations On Internal Cognitive Structures for Language Aquisition

Control of impulsive contact force between mobile manipulator and environment using effective mass and damping controls

Recently, mobile manipulators are being widely employed for various service robots in human environments. Safety is the most important requirement for the operation of mobile robot in a human-populated environment. Indeed, safe human-machine interaction is one of grand challenges in robotics research. This paper proposes a novel control method to reduce impulsive compact force between a mobile manipulator and its environment by using optimized manipulator inertia and damping-based motion control. To find the optimized configuration through null space motion, the combined potential function method is proposed considering both the minimum effective mass and joint limit constraints. The results of this study show that the inertia optimization along with a damping controller significantly reduces the impulsive force upon collision and the contact force after collision.

“Ordinary biodiversity” in western Burkina Faso (West Africa): what vegetation do the state forests conserve?

The role of state forests in the conservation of the savannah environment of western Burkina Faso (West Africa) was assessed by considering the customary and fundamental biological components of plant diversity. The focus is on the “ordinary biodiversity” which constitutes the current environment of human populations and which generates their resources. Overall floristic composition, α, β, γ diversities and species accumulation curves of landscape units were compared inside and outside protected areas. Common species were identified according to their frequencies in landscape units and their local abundance. The occurrences of functional (life form, dispersion, etc.) and human-valued (current uses, potential threats, etc.) traits were compared. Current land uses outside protected areas have an unequal effect on the various components of plant diversity. Species richnesses and specific structures were all the more altered for woody species as agricultural pressure is high; conversely, they were maintained for herbaceous communities for which the proportion of wide-ranging species increased. The preservation of useful plant species on cultivated lands was obvious for woody species with edible fruits but less tangible for timber and service wood. It was deficient for species that furnish other non-wood products (e.g. leaves used as food or fodder), the conservation of which may be jeopardized where anthropic pressure is high. Given current land uses and dynamics, maintaining conservation areas in land use planning is crucial in order to preserve ordinary biodiversity.

Molar microwear pattern and palaeoecology of ungulates from La Berbie (Dordogne, France): environment of Neanderthals and modern human populations of the Middle/Upper Palaeolithic

This study contributes to the characterization of the environment of the Middle/Upper Palaeolithic transition in southwestern France through the reconstruction of ungulate diets. Molar microwear analysis is used to characterize the diets of herbivorous mammals from La Berbie. The data for this study were collected through examination of molar facets using white-light confocal microscopy. The results for 22 fossil specimens (representing four species) are compared with those of 59 living ruminants. Microwear variables of living specimens are used in a principal components analysis. Extinct species are then added to the analysis. Results show that Bison priscus has a microwear signature similar to that of grazers, whereas Rangifer tarandus shares characteristics with both browsers and grazers. The specimens of Equus caballus and Rupicapra rupicapra also display microwear patterns similar to those of grazers. While previous studies have shown an abundance of both C3 plants and herbaceous dicots, the present microwear analysis of fossil herbivores indicates that their last meals contained abrasive graminoids. The results suggest that during the Middle/Upper Palaeolithic transition the habitat of Neanderthals and modern human populations in southwestern France was an open environment dominated by a rich C3-monocot herbaceous layer.

HUMAN–ROBOT COLLABORATION: A SURVEY

As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. They do not only have to operate efficiently and safely in natural, populated environments, but also be able to achieve higher levels of cooperation and communication with humans. Human–robot collaboration (HRC) is a research field with a wide range of applications, future scenarios, and potentially a high economic impact. HRC is an interdisciplinary research area comprising classical robotics, cognitive sciences, and psychology. This paper gives a survey of the state of the art of HRC. Established methods for intention estimation, action planning, joint action, and machine learning are presented together with existing guidelines to hardware design. This paper is meant to provide the reader with a good overview of technologies and methods for HRC.

Molar microwear pattern and palaeoecology of ungulates from La Berbie (Dordogne, France): environment of Neanderthals and modern human populations of the Middle/Upper Palaeolithic

Molar microwear pattern and palaeoecology of ungulates from La Berbie (Dordogne, France): environment of Neanderthals and modern human populations of the Middle/Upper Palaeolithic

Pre-emptive versus non-pre-emptive real time scheduling in intelligent mobile robotics

Autonomous and semi-autonomous mobile robots have to perform a multiplicity of concurrent activities in order to carry out useful tasks in unstructured human-populated environments. Even if it is commonly accepted that a successful accomplishment of assigned tasks requires some sort of real time capability to quickly react and adapt to environmental changes, it is not clear which operating system support is best suited for the scheduling and synchronizing of concurrent activities with different timing requirements. This paper discusses this problem, comparing two different real time scheduling policies for autonomous robot applications: pre-emptive rate monotonic and non pre-emptive Earliest Deadline First (EDF). Experimental results are presented and evaluated.