We present a deep autoencoder-based anomaly detection method (GridNet) for indoor surveillance. Unlike similar studies, GridNet is image-agnostic by taking a specific representation of a scene as inputs instead of the...
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We present a deep autoencoder-based anomaly detection method (GridNet) for indoor surveillance. Unlike similar studies, GridNet is image-agnostic by taking a specific representation of a scene as inputs instead of the raw image itself. Its input is grid representations of scene images, which indicate spatial layouts of objects in a scene. This approach allows us to isolate the anomaly detection problem from any vision-related issues, such as illumination variations. In addition to grid representations, GridNet takes a location vector of a scene as input to learn the normalities of each scene conditioned on its location. We also propose a novel loss function that increases the model's reconstruction capability for grid representations. It enables the network to increase its precision and recall throughout the reconstruction. In our experiments, we compare our method with the existing studies on simulated and real-world data. The experimental results show the superiority of our method compared to the baseline methods. The code, data, and simulation environments will be available at https://***/gridnet.
Full-dimensional natural arm manipulation is a challenging task in the field of model-based control due to its high degree of freedom and unknown dynamics of the given system. deep reinforcement learning (DRL) offers ...
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Full-dimensional natural arm manipulation is a challenging task in the field of model-based control due to its high degree of freedom and unknown dynamics of the given system. deep reinforcement learning (DRL) offers a promising model-free approach for handling high-dimensional robotics problems. Although impressive results for the arm manipulation task have been reported, it still remains an open problem on how we can create human-like synergetic reaching motion using learning algorithms. In this study, we apply DRL for managing full-dimensional arm manipulation in a simulation study, and verify the relations among motion error, energy, and synergy emergence, to reveal the mechanism of employing motor synergy. Although synergy information has never been encoded into the reward function, the synergy naturally emerges along with feedforward control, leading to a similar situation as human motion learning. To the best of our knowledge, this is a pioneer study demonstrating the error and energy optimization issue exists behind the motor synergy employment in DRL for reaching tasks. In addition, our proposed feedback-augmented DRL controller shows better capability over DRL in terms of synergy development and the coupled criteria of error-energy index. This implies that feedback control can support the learning process under redundancy by voiding unnecessary random exploration.
In this paper, we propose a system which detects and estimates the kinematic structures of objects in the indoor environment. We are interested in specific types of objects like doors, sliding doors, and drawers which...
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ISBN:
(纸本)9781728189567
In this paper, we propose a system which detects and estimates the kinematic structures of objects in the indoor environment. We are interested in specific types of objects like doors, sliding doors, and drawers which are common in the human environment and very important taking into account the full autonomy of mobile robots. We assume that the mobile robot is equipped with an RGB-D camera. We utilize a Convolutional Neural Network-based (CNN-based) object detector to locate the articulated objects on the input image created from a pair of RGB-D images. Taking into account strong prior knowledge about the articulated object, we detect the segments on the image which belong to the articulated object. Then, the optimization-based procedure finds the 3D pose and configuration of the joint detected on the scene. We train and verify the method on the images from the Kinect sensor. The performance of the proposed method shows that we can estimate articulated objects in the indoor environment using typical sensors available on the mobile robot.
In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approa...
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ISBN:
(纸本)9789897583803
In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approach. We use separate deep neural networks to: detect the object on the image, estimate the center of the object, and estimate the translation and "in-place" rotation of the object. Then, we use geometrical relations on the image and the camera model to recover the full 6D object pose. As a result, we avoid the direct estimation of the object orientation defined in SO3 using a neural network. We propose the 4D-NET neural network to estimate translation and "in-place" rotation of the object. Finally, we show results on the images generated from the Pascal VOC and ShapeNet datasets.
In this work, we propose a novel strategy to ensure infants, who inadvertently displace their quilts during sleep, are promptly and accurately re-covered. Our approach is formulated into two subsequent steps: interfer...
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ISBN:
(纸本)9798350384581;9798350384574
In this work, we propose a novel strategy to ensure infants, who inadvertently displace their quilts during sleep, are promptly and accurately re-covered. Our approach is formulated into two subsequent steps: interference resolution and quilt spreading. By leveraging the DWPose human skeletal detection and the Segment Anything instance segmentation models, the proposed method can accurately recognize the states of the infant and the quilt over her, which involves addressing the interferences resulted from an infant's limbs laid on part of the quilt. Building upon prior research, the EM*D deeplearning model is employed to forecast quilt state transitions before and after quilt spreading actions. To improve the sensitivity of the network in distinguishing state variation of the handled quilt, we introduce an enhanced loss function that translates the voxelized quilt state into a more representative one. Both simulation and real-world experiments validate the efficacy of our method, in spreading and recover a quilt over an infant.
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