6,913 research outputs found
Incremental Learning for Robot Perception through HRI
Scene understanding and object recognition is a difficult to achieve yet
crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have
shown success in this task. However, there is still a gap between their
performance on image datasets and real-world robotics scenarios. We present a
novel paradigm for incrementally improving a robot's visual perception through
active human interaction. In this paradigm, the user introduces novel objects
to the robot by means of pointing and voice commands. Given this information,
the robot visually explores the object and adds images from it to re-train the
perception module. Our base perception module is based on recent development in
object detection and recognition using deep learning. Our method leverages
state of the art CNNs from off-line batch learning, human guidance, robot
exploration and incremental on-line learning
Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition
This paper presents a self-supervised method for visual detection of the
active speaker in a multi-person spoken interaction scenario. Active speaker
detection is a fundamental prerequisite for any artificial cognitive system
attempting to acquire language in social settings. The proposed method is
intended to complement the acoustic detection of the active speaker, thus
improving the system robustness in noisy conditions. The method can detect an
arbitrary number of possibly overlapping active speakers based exclusively on
visual information about their face. Furthermore, the method does not rely on
external annotations, thus complying with cognitive development. Instead, the
method uses information from the auditory modality to support learning in the
visual domain. This paper reports an extensive evaluation of the proposed
method using a large multi-person face-to-face interaction dataset. The results
show good performance in a speaker dependent setting. However, in a speaker
independent setting the proposed method yields a significantly lower
performance. We believe that the proposed method represents an essential
component of any artificial cognitive system or robotic platform engaging in
social interactions.Comment: 10 pages, IEEE Transactions on Cognitive and Developmental System
GazeDrone: Mobile Eye-Based Interaction in Public Space Without Augmenting the User
Gaze interaction holds a lot of promise for seamless human-computer interaction. At the same time, current wearable mobile eye trackers require user augmentation that negatively impacts natural user behavior while remote trackers require users to position themselves within a confined tracking range. We present GazeDrone, the first system that combines a camera-equipped aerial drone with a computational method to detect sidelong glances for spontaneous (calibration-free) gaze-based interaction with surrounding pervasive systems (e.g., public displays). GazeDrone does not require augmenting each user with on-body sensors and allows interaction from arbitrary positions, even while moving. We demonstrate that drone-supported gaze interaction is feasible and accurate for certain movement types. It is well-perceived by users, in particular while interacting from a fixed position as well as while moving orthogonally or diagonally to a display. We present design implications and discuss opportunities and challenges for drone-supported gaze interaction in public
A motion system for social and animated robots
This paper presents an innovative motion system that is used to control the motions and animations of a social robot. The social robot Probo is used to study Human-Robot Interactions (HRI), with a special focus on Robot Assisted Therapy (RAT). When used for therapy it is important that a social robot is able to create an "illusion of life" so as to become a believable character that can communicate with humans. The design of the motion system in this paper is based on insights from the animation industry. It combines operator-controlled animations with low-level autonomous reactions such as attention and emotional state. The motion system has a Combination Engine, which combines motion commands that are triggered by a human operator with motions that originate from different units of the cognitive control architecture of the robot. This results in an interactive robot that seems alive and has a certain degree of "likeability". The Godspeed Questionnaire Series is used to evaluate the animacy and likeability of the robot in China, Romania and Belgium
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