Francesco Riccio

Multi-robot search for a moving target: Integrating world modeling, task assignment and context

In this paper, we address coordination within a team of cooperative autonomous robots that need to accomplish a common goal. Our survey of the vast literature on the subject highlights two directions to further improve the performance of a multi-robot team. In particular, in a dynamic environment, coordination needs to be adapted to the different situations at hand (for example, when there is a dramatic loss of performance due to unreliable communication network). To this end, we contribute a novel approach for coordinating robots. Such an approach allows a robotic team to exploit environmental knowledge to adapt to various circumstances encountered, enhancing its overall performance. This result is achieved by dynamically adapting the underlying task assignment and distributed world representation, based on the current state of the environment. We demonstrate the effectiveness of our coordination system by applying it to the problem of locating a moving, non-adversarial target. In particular, we report on experiments carried out with a team of humanoid robots in a soccer scenario and a team of mobile bases in an office environment.

STAM: A Framework for Spatio-Temporal Affordance Maps

Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances STA and Spatio-Temporal Affordance Map STAM. Using this formalism, we encode action semantics related to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that affordances encode accurate semantics of the environment.

Enabling Symbiotic Autonomy in Short-Term Interactions: A User Study

The presence of robots in everyday environments is increasing day by day, and their deployment spans over various applications: industrial and working scenarios, health care assistance in public areas or at home. However, robots are not yet comparable to humans in terms of capabilities; hence, in the so-called Symbiotic Autonomy, robots and humans help each other to complete tasks. Therefore, it is interesting to identify the factors that allow to maximize human-robot collaboration, which is a new point of view with respect to the HRI literature and very much leaning toward a social behavior. In this work, we analyze a subset of such variables as possible influencing factors of humans’ Collaboration Attitude in a Symbiotic Autonomy framework, namely: Proxemics setting, Activity Context, and Gender and Height as valuable features of the users. We performed a user study that takes place in everyday environments expressed as activity contexts, such as relaxing and working ones. A statistical analysis of the collected results shows a high dependence of the Collaboration Attitude in different Proxemics settings and Gender.

Learning human-robot handovers through π-STAM: Policy improvement with spatio-temporal affordance maps

Human-robot handovers are characterized by high uncertainty and poor structure of the problem that make them difficult tasks. While machine learning methods have shown promising results, their application to problems with large state dimensionality, such as in the case of humanoid robots, is still limited. Additionally, by using these methods and during the interaction with the human operator, no guarantees can be obtained on the correct interpretation of spatial constraints (e.g., from social rules). In this paper, we present Policy Improvement with Spatio-Temporal Affordance Maps - π-STAM, a novel iterative algorithm to learn spatial affordances and generate robot behaviors. Our goal consists in generating a policy that adapts to the unknown action semantics by using affordances. In this way, while learning to perform a human-robot handover task, we can (1) efficiently generate good policies with few training episodes, and (2) easily encode action semantics and, if available, enforce prior knowledge in it. We experimentally validate our approach both in simulation and on a real NAO robot whose task consists in taking an object from the hands of a human. The obtained results show that our algorithm obtains a good policy while reducing the computational load and time duration of the learning process.

Context in Robotics and Information Fusion

Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions, and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the design and implementation of architectures for information fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual information into a framework for information fusion.