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Often overlooked in human-robot interaction is the challenge of people detection. For natural interaction, a robot must detect people without waiting for them to face the camera, get far enough away to be fully present, or center themselves fully within the field of view. Furthermore, it must happen without requiring immense amounts of processing that are not practical for real systems. In this work...
In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights...
People detection in 2D laser range data is a popular cue for person tracking in mobile robotics. Many approaches are designed to detect pairs of legs. These approaches perform well in many public environments. However, we are working on an assistance robot for stroke patients in a rehabilitation center, where most of the people need walking aids. These tools occlude or touch the legs of the patients...
Recognizing a person from a distance is important to establish meaningful social interaction and to provide additional cues regarding the situations experienced by a robot. To do so, face recognition and speaker identification are biometrics commonly used, with identification performance that are influenced by the distance between the person and the robot. This paper presents a system that combines...
In this paper, we study how human-robot interaction can be beneficial on the Continuous Goal-Directed Actions (CGDA) framework. Specifically, a system for robot discovery of motor primitives from random human-guided movements has been developed. These guided motor primitives (GMP) are used as scaffolds to reproduce a goal-directed actions. CGDA encodes goals as the changes produced on object features...
In human - robot interaction many different signals are used by robots in order to get a proper environment representation. Even more so, the social robots, whose primary task is to maintain successful interaction with humans, should use input signals in order to model the human itself. One important human characteristic is the human emotional state. As a result, perceiving human emotions is one challenging...
In this paper, an indoor navigation algorithm is proposed for the purpose of robot autonomous path planning. Due to the complex situation in indoor environments, it can cause a serious trouble for robot to identify the route during patrolling, especially for corner and door detection, which is the key step for intelligent navigation. To solve this problem, a kinect sensor is used for the door detection...
This paper proposes a solution to increase safety when using robotic walking aids. Walking aids are an important tool as long as they can be safely operated, but elderly people often discard them due to the fear of falling. The authors propose a safety system that locks the walker and giving the appropriate feedback when the user is inadequately gripping the walker's grips. It is based on a low-cost...
Gesture recognition is an important task in Human-Robot Interaction (HRI) and the research effort towards robust and high-performance recognition algorithms is increasing. In this work, we present a neural network approach for learning an arbitrary number of labeled training gestures to be recognized in real time. The representation of gestures is hand-independent and gestures with both hands are...
Automated human activity recognition is an essential task for Human Robot Interaction (HRI). A successful activity recognition system enables an assistant robot to provide precise services. In this paper, we present a two-layered approach that can recognize sub-level activities and high-level activities successively. In the first layer, the low-level activities are recognized based on the RGB-D video...
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