In this paper, we present a framework for learning and replicating simple human movements by an artificial agent (humanoid robot Pepper) and discuss measures based on a sensorimotor Turing Test (smTT) to distinguish between human-generated movements and those generated by an artificial agent.

Since the introduction of the Turing Test to measure machine intelligence, more and more sophisticated artificial systems have been developed to pass the test. These systems revealed some limitations of the Turing Test and new versions of the test have been developed over time in an attempt to overcome these shortcomings. Yet, all these variants still rely on the subjective judgments of human interrogators which are subject to biases. Here, we propose the brain-based Turing Test, a novel version of the test that uses implicit information encoded in the human brain to discriminate between human and artificial agents. We highlight multiple benefits of the brain-based Turing Test, outline its possible outcomes, present an empirical test using robot and human interactive communication, and explain how research in human-robot interaction can profit from it.

Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective -that is, forming robot trust in interaction partners. Our previous robot trust model relies on assessing the trustworthiness of the interaction partners based on the computational cognitive load incurred during the interactive task [1]. However, this model does not take into account the social markers indicative of trustworthiness, such as the gestures displayed by a human partner. In this study, we make a step toward this point by extending the model by integrating a social cue processing module to achieve social human-robot interaction. This new model serves as a novel social cognitive trust framework to enable the Pepper robot to evaluate the trustworthiness of its interaction partners based on both cognitive load (i.e., the cost of perceptual processing) and social cues (i.e., their gestures). For evaluating the efficacy of the framework, the Pepper robot with the developed model is put to interact with human partners who may take the roles of a reliable, unreliable, deceptive, or random suggestion providing partner. Overall, the results indicate that the proposed framework allows the Pepper robot to differentiate the guiding strategies of the partners by detecting deceptive partners and thus select a trustworthy partner in case of a free choice to perform the next task.

One of the fundamental modes of learning in children is through curiosity. Children (and adults) interact with new people, learn about novel objects, activities and other stimuli through curiosity and other intrinsic motivations. Creating autonomous robots that learn continually through intrinsic curiosity may result in breakthroughs in artificial intelligence. Such robots could continue to learn about themselves and the world around them through curiosity, thus improving their abilities over their ‘lifetime’. Although recent works on curiosity in different fields have produced significant results, most of these works have focused on constrained simulated environments which do not involve human interaction. However, in real-world applications such as healthcare, home-assistance etc., robots generally have to interact with humans on a regular basis. In these scenarios, it is imperative that curiosity is directed towards seeking out and learning important information from the humans when needed rather than simply learning in an unsupervised manner. Further, there is limited work on how humans perceive such curious robots and whether humans prefer curious robots that adapt over time to other robots that simply perform their assigned tasks. In this workshop, our goal is to bring together researchers and practitioners in different multidisciplinary fields to discuss the role of robot curiosity in real-world applications and its implications in human-robot interaction (HRI).

In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner who has different guiding strategies: reliable, unreliable, and random. Here, the humanoid robot is equipped with a multimodal auto-associative memory module to process audio-visual patterns to extract cognitive load (i.e., computational cost) and an internal reward module to perform cost-guided reinforcement learning. After interactive experiments, the robot associates a low cognitive load (i.e., high cumulative reward) yielded during the interaction with high trustworthiness of the guiding strategy of the partner. At the end of the experiment, we provide a free choice to the robot to select a trustworthy instructor. We show that the robot forms trust in a reliable partner. In the second setting of the same experiment, we endow the robot with an additional simple theory of mind module to assess the efficacy of the instructor in helping the robot perform the task. Our results show that the performance of the robot is improved when the robot bases its action decisions on factoring in the instructor assessment.

Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in human-robot and robot-robot interaction studies. In this paper, we provide a unified approach to address these issues in robot-robot interaction settings. To be concrete, we present a trust-based affective computational account of scaffolding while performing a sequential visual recalling task. In that, we endow the Pepper humanoid robot with cognitive modules of auto-associative memory and internal reward generation to implement the trust model. The former module is an instance of a cognitive function with an associated neural cost determining the cognitive load of performing visual memory recall. The latter module uses this cost to generate an internal reward signal to facilitate neural cost-based reinforcement learning (RL) in an interactive scenario involving online instructors with different guiding strategies: reliable, less-reliable, and random. These cognitive modules allow the Pepper robot to assess the instructors based on the average cumulative reward it can collect and choose the instructor that helps reduce its cognitive load most as the trustworthy one. After determining the trustworthy instructor, the Pepper robot is recruited to be a caregiver robot to guide a perceptually limited infant robot (i.e., the Nao robot) that performs the same task. In this setting, we equip the Pepper robot with a simple theory of mind module that learns the state-action-reward associations by observing the infant robot’s behavior and guides the learning of the infant robot, similar to when it went through the online agent-robot interactions. The experiment results on this robot-robot interaction scenario indicate that the Pepper robot as a caregiver leverages the decision-making policies – obtained by interacting with the trustworthy instructor– to guide the infant robot to perform the same task efficiently. Overall, this study suggests how robotic-trust can be grounded in human-robot or robot-robot interactions based on cognitive load, and be used as a mechanism to choose the right scaffolding agent for effective knowledge transfer.

Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.

Cortical neurons are silent most of the time: sparse activity enables low-energy computation in the brain, and promises to do the same in neuromorphic hardware. Beyond power efficiency, sparse codes have favourable properties for associative learning, as they can store more information than local codes but are easier to read out than dense codes. Auto-encoders with a sparse constraint can learn sparse codes, and so can single-layer networks that combine recurrent inhibition with unsupervised Hebbian learning. But the latter usually require fast homeostatic plasticity, which could lead to catastrophic forgetting in embodied agents that learn continuously. Here we set out to explore whether plasticity at recurrent inhibitory synapses could take up that role instead, regulating both the population sparseness and the firing rates of individual neurons. We put the idea to the test in a network that employs compartmentalised inputs to solve the task: rate-based dendritic compartments integrate the feedforward input, while spiking integrate-and-fire somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings confirm that intrinsic homeostatic plasticity is not strictly required for regulating sparseness: inhibitory synaptic plasticity can have the same effect. Our work illustrates the usefulness of compartmentalised inputs, and makes the case for moving beyond point neuron models in artificial spiking neural networks.

In this paper we show a new perspective on human-robot interaction by presenting a system for intuitive interaction with flying robots. This goes beyond the usual remote control of these robots, by having an interactive space where people can physically interact with flying robots in an intuitive and safe way. The presented system has various ways of interaction and provides the prerequisites for many interesting future applications.

The video shows the interaction with a customized Kompa\"{i} robot. The robot consists of the Robosoft's robuLAB10 platform, tablet PC, and a Microsoft Kinect camera mounted on a pan-tilt system. A visual control algorithm provides continuous person tracking. The newly developed robot features include gesture recognition, person following, navigation with pointing, and force control, which were integrated with the Robosoft's robuBOX SDK and the Karto SLAM algorithms. The video demonstrates all the features and puts the robot in use in an everyday home scenario.

Hand-eye coordination is an important motor skill acquired in infancy which precedes pointing behavior. Pointing facilitates social interactions by directing attention of engaged participants. It is thus essential for the natural flow of human-robot interaction. Here, we attempt to explain how pointing emerges from sensorimotor learning of hand-eye coordination in a humanoid robot. During a body babbling phase with a random walk strategy, a robot learned mappings of joints for different arm postures. Arm joint configurations were used to train biologically inspired models consisting of SOMs. We show that such a model implemented on a robotic platform accounts for pointing behavior while humans present objects out of reach of the robot's hand.

We propose a computational model based on inverse-forward model pairs for the simulation and execution of actions. The models are implemented on a humanoid robot and are used to control reaching actions with the arms. In the experimental setup a tool has been attached to the left arm of the robot extending its covered action space. The preliminary investigations carried out aim at studying how the use of tools modifies the body scheme of the robot. The system performs action simulations before the actual executions. For each of the arms, predicted end-effector positions are compared with the desired one and the internal pair presenting the lowest error is selected for action execution. This allows the robot to decide on performing an action either with its hand alone or with the one with the attached tool.

Human interaction is highly intuitive: we infer reactions of our opponents mainly from what we have learned in years of experience and often assume that other people have the same knowledge about certain situations, abilities, and expectations as we do. In human-robot interaction (HRI) we cannot take for granted that this is equally true since HRI is asymmetrical. In other words, robots have different abilities, knowledge, and expectations than humans. They need to react appropriately to human expectations and behaviour. With this respect, scientific advances have been made to date for applications in entertainment and service robotics that largely depend on intuitive interaction. However, HRI today is often still unnatural, slow, and unsatisfactory for the human interlocutor. Both the sensorimotor interaction with environment and interlocutor, and the social aspects of the interaction still need to be researched and improved. Therefore, this full-day workshop aims to bring together researchers from different scientific fields to discuss these crosscutting issues and to exchange views on what are the preconditions and principles of intuitive interaction.

Motor Babbling has been identified as a self-exploring behaviour adopted by infants and is fundamental for the development of more complex behaviours, self-awareness and social interaction skills. Here, we adopt this paradigm for the learning strategies of a humanoid robot that maps its random arm movements with its head movements, determined by the perception of its own body. Finally, we analyse three random movement strategies and experimentally test on a humanoid robot how they affect the learning speed.

Emergence is a concept that is not easy to grasp, since it contradicts our idea of central control and planning. In this work, we use a swarm of robots as a tangible tool to visualize interactions as the underlying principle of emergence.

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.

Proper usability evaluations of spoken dialogue systems are costly and cumbersome to carry out. In this paper, we present a new approach for facilitating usability evaluations which is based on user error simulations. The idea is to replace real users with simulations derived from empirical observations of users ’ erroneous behavior. The simulated errors must cover both system-driven errors (e.g., due to poor speech recognition) as well as conceptual errors and slips of the user, because neither alone is predictive of perceived usability. The simulation is integrated into a workbench which produces reports of typical and rare errors, and which allows usability ratings to be predicted. If successful, this workbench will help designers in making choices between system versions and lower testing costs at early phases of development. Challenges to the approach are discussed and solutions proposed. Index Terms: spoken-dialogue system, evaluation, usability 1.

Biological evidence strongly suggests that insects utilize visual cues for their navigation tasks. This paper discusses the evolution of a simple controller for visual homing by means of evolutionary algorithms. The application is representative for a class of (real world) problems, for which the choice of the fitness function is non-trivial, since the data are not known in advance. For this class of problems, recombination has a much higher influence on the convergence than previously assumed. We show how convergence rates comparable to those of neural network learning algorithms can be achieved.

In this paper, we present a first series of experiments with prototype artificial whiskers that have been developed in our laboratory. These experiments have been inspired by neuroscience research on real rats. In spite of the enormous potential of whiskers, they have to date not been systematically investigated and exploited by roboticists. Although the transduction mechanism is simple and straightforward, and the whiskers are currently used in a passive way only, the dynamics of the sensory signals resulting from the interaction with various textured surfaces is complex and has a rich information content. The experiments provide the foundation for future work including active sensing, whisker arrays, and cross-modal integration.