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In this paper, we present Rapid Activity Prediction Through Object-oriented Regression (RAPTOR), a scalable method for performing rapid, real-time activity recognition and prediction that achieves state-of-the-art classification accuracy on both a generic human activity dataset and two domain-specific collaborative robotics manufacturing datasets. Our approach is designed to be human-interpretable:...
Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence...
This paper addresses bird song analysis based on semi-automatic annotation. Research in animal behavior, especially with birds, would be aided by automated (or semiautomated) systems that can localize sounds, measure their timing, and identify their source. This is difficult to achieve in real environments where several birds may be singing from different locations and at the same time. Analysis of...
Currently, studies on learning relationship between objects focus on the text domain. There are a few researchers who focus on relationship learning between objects in other domains. In these researches, they have tried to represent the qualitative description of structure of objects, and the symbolic relationship between them. This output provides symbolic meaning to the inter-object relationships...
This paper defines a model-based control hap tic guidance (MPC-HG) approach to improve the performance of a user in a minimally invasive surgery (MIS) training while performing a surgical related task. In this approach, a robot applies controlled forces on the hand of the user to guide him/her through an MIS training task according to an MIS reference model and desired set of motions. The main challenges...
This paper focuses on recognition and prediction of human reaching motion in industrial manipulation tasks. Several supervised learning methods have been proposed for this purpose, but we seek a method that can build models on-the-fly and adapt to new people and new motion styles as they emerge. Thus, unlike previous work, we propose an unsupervised online learning approach to the problem, which requires...
In recent years there has been increased interest in studies that explore integrative learning of language and other modalities by using neural network models. However, for practical application to human-robot interaction, the acquired semantic structure between language and meaning has to be available immediately and repeatably whenever necessary, just as in everyday communication. As a solution...
Automated segmentation and recognition of fine-grained activities is important for enabling new applications in industrial automation, human-robot collaboration, and surgical training. Many existing approaches to activity recognition assume that a video has already been segmented and perform classification using an abstract representation based on spatio-temporal features. While some approaches perform...
Human guide robots need to generate a trajectory from human training. The popular work space methods have to calculate the inverse kinematics. While the joint space methods need the dynamic time warping. These destroy the accuracy of the trajectory model. In this paper, we use Lloyd's algorithm to hidden Markov model (HMM). The advantages of the method over the other HMM are the time difference does...
Task recognition and future human activity prediction are of importance for a safe and profitable human-robot cooperation. In real scenarios, the robot has to extract this information merging the knowledge of the task with contextual information from the sensors, minimizing possible misunderstandings. In this paper, we focus on tasks that can be represented as a sequence of manipulated objects and...
Learning from demonstrations is an intuitive way for instructing robots by non-experts. One challenge in learning from demonstrations is to infer what to imitate, especially when the robot only observes the teacher and does not have further knowledge about the demonstrated actions. In this paper, we present a novel approach to the problem of inferring what to imitate to successfully reproduce a manipulation...
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...
Human gesture recognition is of importance for smooth and efficient human robot interaction. One of difficulties in gesture recognition is that different actors have different styles in performing even same gestures. In order to move towards more realistic scenarios, a robot is required to handle not only different users, but also different view points and noisy incomplete data from onboard sensors...
A robotic scrub nurse (RSN) designed for safe human-robot collaboration in the operating room (OR) is presented. The RSN assists the surgical staff in the OR by delivering instruments to the surgeon and operates through a multimodal interface allowing instruments to be requested through verbal commands or touchless gestures. A machine vision algorithm was designed to recognize the hand gestures performed...
This paper proposes a novel way of controllable pitch re-estimation that can produce better pitch contour or provide diverse speaking styles for text-to-speech (TTS) systems. The method is composed of a pitch re-estimation model and a set of control parameters. The pitch re-estimation model is employed to reduce over-smoothing effects which is usually introduced by TTS training. The control parameters...
State estimation and control are intimately related processes in robot handling of flexible and articulated objects. While for rigid objects, we can generate a CAD model before-hand and a state estimation boils down to estimation of pose or velocity of the object, in case of flexible and articulated objects, such as a cloth, the representation of the object's state is heavily dependent on the task...
This paper presents to apply Automatic Speech Recognition (ASR) algorithm to the meal service robot so that the user can be easier to order the meal and increase interaction between the robot and human. The Mel-frequency Cepstral coefficients (MFCC) is used to scratch the feature parameters, and Hidden Markov Model (HMM) is applied as the recognition speech model via Spce3200. It is based on the embedded...
Researches in the brain science field have uncovered the human capability to use tools as if they are part of the human bodies (known as tool–body assimilation) through trial and experience. This paper presents a method to apply a robot's active sensing experience to create the tool–body assimilation model. The model is composed of a feature extraction module, dynamics learning module, and a tool–body...
A social intelligent robot should be capable of observing and understanding the changes in the environment so as to behave in a proper manner. It also needs to take into account user preferences, user disability level, and user profile. This paper presents a research work based on socially assistive robotics (SAR) technology that aims at providing affordable personalized physical and cognitive assistance,...
This paper proposes a HMM-based user's information demand estimation model for autonomous informational assistance robots to avoid providing information prematurely. The model estimates the user's implicit information demands by predicting a user's next information request using user's head movements. Through a word-association quiz-dialog experiment, our model demonstrated superior prediction performance...
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