IEEE Transactions on Cognitive and Developmental Systems

Given recent technological developments in robotics, artificial intelligence, and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In... View full abstract»

A human being's cognitive system can be simulated by artificial intelligent systems. Machines and robots equipped with cognitive capability can automatically recognize a humans mental state through their gestures and facial expressions. In this paper, an artificial intelligent system is proposed to monitor depression. It can predict the scales of Beck depression inventory II (BDI-II) from vocal an... View full abstract»

Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision syste... View full abstract»

Automatic driving vehicles have been developed to provide more convenient and comfortable driving experiences. However, these vehicles failed in satisfying the variance of human intentions. Recently, the strategy of collaborating brain-computer interface (BCI) controlling and automatic driving receives attention. Since the BCI system remained some limitation in real-time controlling, a fusion meth... View full abstract»

Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state-of-the-art work within the... View full abstract»

Affordances capture the relationships between a robot and the environment in terms of the actions that the robot is able to perform. The notable characteristic of affordance-based perception is that an object is perceived by what it affords (e.g., graspable and rollable), instead of identities (e.g., name, color, and shape). Affordances play an important role in basic robot capabilities such as re... View full abstract»

Reinforcement learning (RL) problems are hard to solve in a robotics context as classical algorithms rely on discrete representations of actions and states, but in robotics both are continuous. A discrete set of actions and states can be defined, but it requires an expertise that may not be available, in particular in open environments. It is proposed to define a process to make a robot build its ... View full abstract»

The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interacti... View full abstract»

Dynamic uncontrolled human-robot interactions (HRIs) require robots to be able to adapt to changes in the human's behavior and intentions. Among relevant signals, nonverbal cues such as the human's gaze can provide the robot with important information about the human's current engagement in the task, and whether the robot should continue its current behavior or not. However, robot reinforcement le... View full abstract»

In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG feature vectors. In contrast to conventional CCA m... View full abstract»

We present a biologically inspired and scalable model of the basal ganglia (BG) simulated on the spiking neural network architecture (SpiNNaker) machine, a biologically inspired low-power hardware platform allowing parallel, asynchronous computing. Our BG model consists of six cell populations, where the neuro-computational unit is a conductance-based Izhikevich spiking neuron; the number of neuro... View full abstract»

How can robotics benefit from neuroscience to build a unified framework that computes actions for both, locomotion and manipulation tasks? Inspired by the hierarchical neural control of movement from cortex to spinal cord, we propose a model that generates a concrete action representation in robotics. The action program is composed of four basic modules: pattern selection, spatial coordination, te... View full abstract»

An integrated biologically inspired self-organizing map (SOM) algorithm is proposed for task assignment and path planning of an autonomous underwater vehicle (AUV) system in 3-D underwater environments with obstacle avoidance. The algorithm embeds the biologically inspired neural network (BINN) into the SOM neural networks. The task assignment and path planning aim to arrange a team of AUVs to vis... View full abstract»

In the future, robots will be used more extensively as assistants in home scenarios and must be able to acquire expertise from trainers by learning through crossmodal interaction. One promising approach is interactive reinforcement learning (IRL) where an external trainer advises an apprentice on actions to speed up the learning process. In this paper we present an IRL approach for the domestic ta... View full abstract»

People seem to miscalibrate their expectations and interactions with a robot. When it comes to robot design, the anthropomorphism level of the robot form (appearance) has become an increasingly important variable to consider. It is argued here that people base their expectations and perceptions of a robot on its form and attribute functions which do not necessarily mirror the true functions of the... View full abstract»

In this paper, we design an object sorting robot system, which is based on robot operating system distributed processing framework. This system can communicate with human beings; can percept the 3-D environment by Kinect sensor; has the ability of reasoning; can transfer the natural language intention to machine instruction to control the movement of manipulator. In particular, in order to improve... View full abstract»

A path planning algorithm for outdoor robots, which is based on neuronal spike timing, is introduced. The algorithm is inspired by recent experimental evidence for experience-dependent plasticity of axonal conductance. Based on this evidence, we developed a novel learning rule that altered axonal delays corresponding to cost traversals and demonstrated its effectiveness on real-world environmental... View full abstract»

In order for robots to learn from people with no machine learning expertise, robots should learn from natural human instruction. Most machine learning techniques that incorporate explanations require people to use a limited vocabulary and provide state information, even if it is not intuitive. This paper discusses a software agent that learned to play the Mario Bros. game using explanations. Our g... View full abstract»

Emotions play a crucial role in decision-making, brain activity, human cognition, and social intercourse. This paper proposes a hierarchical network structure with subnetwork nodes to discriminate three human emotions: 1) positive; 2) neutral; and 3) negative. Each subnetwork node embedded in the network that are formed by hundreds of hidden nodes, could be functional as an independent hidden laye... View full abstract»

Significant research and development has been invested in technical issues related to person following. However, a systematic approach for designing robotic person-following behavior that maintains appropriate social conventions across contexts has not yet been developed. To understand why this may be the case, an in-depth literature review of 221 articles on person-following robots was performed,... View full abstract»

We draw on a simulationist approach to the analysis of facially displayed emotions, e.g., in the course of a face-to-face interaction between an expresser and an observer. At the heart of such perspective lies the enactment of the perceived emotion in the observer. We propose a novel probabilistic framework based on a deep latent representation of a continuous affect space, which can be exploited ... View full abstract»

Robotic hand-eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand-eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method. Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse chan... View full abstract»

Spiking Neural Networks (SNNs) have emerged as a promising brain inspired neuromorphic-computing paradigm for cognitive system design due to their inherent event-driven processing capability. The fully-connected shallow SNNs typically used for pattern recognition require large number of trainable parameters to achieve competitive classification accuracy. In this work, we propose a deep Spiking Con... View full abstract»

The integration of information across distinct modalities enhances perceptual abilities. An ecologically important role of multisensory integration is in scaffolding verbal communication, which relies upon the precise temporal integration of auditory and visual cues. However, the role of (multi)sensory function in supporting another important aspect of communication, namely, nonverbal communicatio... View full abstract»

A major issue in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the intrinsic nonstationarities in the brain waves, which may degrade the performance of the classifier, while transitioning from calibration to feedback generation phase. The nonstationary nature of the EEG data may cause its input probability distribution to vary over time, which often appear as a covariate shi... View full abstract»