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This paper presents a hierarchal, two-layer, connectionist-based human-action recognition system (CHARS) as a first step towards developing socially intelligent robots. The first layer is a K-nearest neighbor (K-NN) classifier that categorizes human actions into two classes based on the existence of locomotion, and the second layer consists of two multi-layer recurrent neural networks that distinguish...
Ways in which people perceive machines as robots can influence their subsequent behavior and interactions. Individuals may make these classification decisions based solely on visual information, and thus the physical form of the entity alone. Participants viewed images of robots from a variety of identified domains and rated each image according to the extent to which they perceive the entity as a...
Effective human-robot interaction requires systems that can accurately infer and predict human intentions. In this paper, we introduce a system that uses stacked denoising autoencoders to perform intent recognition. We introduce the intent recognition problem, provide an overview of deep architectures in machine learning, and outline the components of our system. We also provide preliminary results...
In all human-robot interaction, trust is an important element to consider because the presence or absence of trust certainly impacts the ultimate outcome of that interaction. Limited research exists that delineates the development and maintenance of this trust in various operational contexts. Our own prior research has investigated theoretical and empirically supported antecedents of human-robot trust...
Robotic systems are being introduced into military echelons to extend warfighter capabilities in complex, dynamic environments. While these systems are designed to complement human capabilities (e.g., aiding in battlefield situation awareness and decision making, etc), they are often misused or disused because the user does not have an appropriate level of trust in his or her robotic counterpart(s)...
Auditory instruction is a well used method for people of all ages because of its understandability. However the additional voice has the possibility to disturb the user's learning during the instruction because it strongly implies the support of third-person helpers. This risk increases with older people because their confidence in their ability may decline compared to the younger people. The authors...
This study explores how human users respond to feedback and evaluation from a robot. A between-subjects experiment was conducted using the Wizard of Oz method, with 63 participants randomly assigned to one of three evaluations (good evaluation vs. neutral evaluation vs. bad evaluation) following a training session. When participants attempted to reproduce the physical motion taught by the robot, they...
We report on the development of a new simulation environment for use in Multi-Robot Learning, Swarm Robotics, Robot Teaming, Human Factors and Operator Training. The simulator provides a realistic environment for examining methods for localization and navigation, sensor analysis, object identification and tracking, as well as strategy development, interface refinement and operator training (based...
This paper proposes a method for interpretation of fuzzy voice commands based on the vocal cues. The fuzzy voice commands include fuzzy linguistic terms like “little” and quantitative meaning of such terms depends on the environmental conditions. Therefore the robot's perception of the corresponding environment is modified by acquiring the user's perception through a series of vocal cues. The user's...
Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural...
In this paper we present a novel approach for hand gesture recognition. The proposed system utilizes upper body part tracking in a 9-dimensional configuration space and two Multi-Layer Perceptron/Radial Basis Function (MLP/RBF) neural network classifiers, one for each arm. Classification is achieved by buffering the trajectory of each arm and feeding it to the MLP Neural Network which is trained to...
Today's household appliances are quickly increasing their features and functions. These new technologies require innovative training methods to maintain learning motivation especially with older users, because they may need more time to learn. Conventional manuals are insufficient to maintain motivation in this population. Previously studied assistive communication robots also have difficulty explaining...
Facial expressions are one important nonverbal communication cue, as they can provide feedback in conversations between people and also in human-robot interaction. This paper presents an evaluation of three standard pattern recognition techniques (active appearance models, gabor energy filters, and raw images) for facial feedback interpretation in terms of valence (success and failure) and compares...
In this paper, we discuss a coordinated haptic training architecture useful for transferring expertise in teleoperation-based manipulation between two human users. The objective is to construct a reality-based haptic interaction system for knowledge transfer by linking an expert's skill with robotic movement in real time. The benefits from this approach include 1) a representation of an expert's knowledge...
In this paper, we propose a novel Adaboost template to recognize human upper body poses from disparity images for natural human robot interaction (HRI). First, the upper body poses of standing persons are classified into seven categories of views. For each category, a mean template, variance template, and percentage template are generated. Then, the template region is divided into positive and negative...
This paper explores supervisory control of multiple, heterogeneous, independent robots by operator teams. Experimental evidence is presented which suggests that two cooperating operators may have free capacity that can be used to improve primary task performance without increasing average fan-out.
Interaction between humans and robots in peer-based teams can be dramatically affected by human performance. Our research is focused on determining if existing human performance moderator functions apply to peer-based human-robot interaction and if not, how such functions must be modified. Our initial work focuses on modeling workload. Validation of the models will require human subject evaluations...
In this paper, we compare user behavior towards the humanoid robot ASIMO and the dog-shaped robot AIBO in a simple task, in which the users has to teach commands and feedback to the robot.
Based on recent studies which establishes that skill acquisition requires not just specification of motor skills, learning and skill application but also intervention of human expert only in certain phases, we present an approach which encode the human expert demonstration into a teacher class based on Fuzzy ArtMap network. Then, the human novice trainee produces the approximate knowledge, which is...
In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed. The objective of this work is to achieve better classification...
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