Mariacarla Staffa

Attentional modulation of mutually dependent behaviors

In this paper, we investigate simple attentional mechanisms suitable for sensing rate regulation and action coordination in the presence of mutually dependent behaviors. We present our architecture along with a case study where a real robotic system is to manage and harmonize conflicting tasks. This research focuses on attentional mechanisms for regulating the frequencies of sensor readings and action activations in a behavior-based robotic system. Such mechanisms are to direct sensors toward the most salient sources of information and filter the available sensory data to prevent unnecessary information processing.

Continuous gesture recognition for flexible human-robot interaction

In this work, we present a reliable and continuous gesture recognition method that supports a natural and flexible interaction between the human and the robot. The aim is to provide a system that can be trained online with few samples and can cope with intra user variability during the gesture execution. The proposed approach relies on the generation of an ad-hoc Hidden Markov Model (HMM) for each gesture exploiting a direct estimation of the parameters. Each model represents the best prototype candidate from the associated gesture training set. The generated models are then employed within a continuous recognition process that provides the probability of each gesture at each step. The proposed method is evaluated in two case studies: a hand-performed letters recognizer and a natural gesture recognizer. Finally, we show the overall system at work in a simple human-robot interaction scenario.

Agent WiSARD in a 3d world

This paper investigates the integration of verbal and visual information for describing (explaining) the content of images formed by three–dimensional geometrical figures, from a hybrid neurosymbolic perspective. The results of visual object classifications involving top–down application of stored knowledge and bottom–up image processing are effectively explained relying on both words and pictures. The latter seems particularly suitable in explanations concerning high–level visual tasks involving both top–down reasoning and bottom–up perceptual processes.

Attentional regulations in a situated human-robot dialogue

We propose a framework where the human-robot interaction is modeled as a multimodal dialogue which is regulated by an attentional system that guides the robot towards the execution of structured tasks. Specifically, we propose an approach where the dialogue between the human and the robot is represented as a Partially Obervable Markov Decision Process (POMDP), while the associated dialogue policy is enhanced by top-down attentional mechanisms that provide contextual and task-related contents. We introduce simple case studies that illustrate the system at work in different conditions considering top-down regulations and dialogue flows in synergistic and conflicting situations.

An Attentional Approach to Human-Robot Interactive Manipulation

Human robot collaborative work requires interactive manipulation and object handover. During the execution of such tasks, the robot should monitor manipulation cues to assess the human intentions and quickly determine the appropriate execution strategies. In this paper, we present a control architecture that combines a supervisory attentional system with a human aware manipulation planner to support effective and safe collaborative manipulation. After detailing the approach, we present experimental results describing the system at work with different manipulation tasks (give, receive, pick, and place).

The role of intrinsic motivations in attention allocation and shifting

The concepts of attention and intrinsic motivations are of great interest within adaptive robotic systems, and can be exploited in order to guide, activate, and coordinate multiple concurrent behaviors. Attention allocation strategies represent key capabilities of human beings, which are strictly connected with action selection and execution mechanisms, while intrinsic motivations directly affect the allocation of attentional resources. In this paper we propose a model of Reinforcement Learning (RL), where both these capabilities are involved. RL is deployed to learn how to allocate attentional resources in a behavior-based robotic system, while action selection is obtained as a side effect of the resulting motivated attentional behaviors. Moreover, the influence of intrinsic motivations in attention orientation is obtained by introducing rewards associated with curiosity drives. In this way, the learning process is affected not only by goal-specific rewards, but also by intrinsic motivations.

Monitoring Strategies for Adaptive Periodic Control in Behavior-Based Robotic Systems

The main goal of our current research is the design of a behavior-based robotic architecture that has the capability of adapting its behaviors to the rate of change of both the environment and its internal states reducing the computational costs of input processing. Inspired by research on biological clocks, we introduced a simple schema theory model where releasing mechanisms are combined with adaptive internal clocks. In this paper, we describe the design and development of a complete robotic architecture implementing this model. In particular, we considered a mobile robot domain that simulates the navigation behavior of a Catagliphys ant enhanced with simple visual capabilities.

Attentional human-robot interaction in simple manipulation tasks

We present a robotic control system endowed with attentional mechanisms suitable for balancing the trade off between safe human-robot interaction and effective task execution. These mechanisms allow the robot to increase or decrease the degree of attention toward relevant activities modulating the frequency of the monitoring rate and the speed associated to the robot movements. In this framework, we consider pick-and-place and give-and-receive attentional behaviors.

Attentional Action Selection Using Reinforcement Learning

Reinforcement learning is typically used to model and optimize action selection strategies, in this work we deploy it to optimize attentional allocation strategies while action selection is obtained as a side effect. We present a reinforcement learning approach to attentional allocation and action selection in a behavior-based robotic systems. We detail our attentional allocation mechanisms describing the reinforcement learning problem and analysing its performance in a survival domain.

Attentional and emotional regulation in human-robot interaction

In this paper, we propose a human-robot interaction system that exploits emotion and attention to regulate and adapt the robotic interactive behavior. In particular, we will focus on the relation between arousal, predictability, and attentional allocation considering as a case study a robotic manipulator interacting with a human operator. We rely on a frequency based model of attention allocation and a 4-dimensional model of emotion. The experiment reported in this paper explores the effectiveness of an attentional regulation mechanisms modulated by arousal and predictability values extracted from the human voice. The collected results show that the attentional modulation, mediated by basic emotional speech features, provides a natural and computationally light regulation mechanism for coordinating the robotic behaviors.