Author name cluster

Chung-Yeon Lee

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4 papers

2 author rows

IJCAI Conference 2022 Conference Paper

PlaceNet: Neural Spatial Representation Learning with Multimodal Attention

Chung-Yeon Lee
Youngjae Yoo
Byoung-Tak Zhang

Spatial representation capable of learning a myriad of environmental features is a significant challenge for natural spatial understanding of mobile AI agents. Deep generative models have the potential of discovering rich representations of observed 3D scenes. However, previous approaches have been mainly evaluated on simple environments, or focused only on high-resolution rendering of small-scale scenes, hampering generalization of the representations to various spatial variability. To address this, we present PlaceNet, a neural representation that learns through random observations in a self-supervised manner, and represents observed scenes with triplet attention using visual, topographic, and semantic cues. We evaluate the proposed method on a large-scale multimodal scene dataset consisting of 120 million indoor scenes, and show that PlaceNet successfully generalizes to various environments with lower training loss, higher image quality and structural similarity of predicted scenes, compared to a competitive baseline model. Additionally, analyses of the representations demonstrate that PlaceNet activates more specialized and larger numbers of kernels in the spatial representation, capturing multimodal spatial properties in complex environments.

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ICRA Conference 2021 Conference Paper

Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots

Youngjae Yoo
Chung-Yeon Lee
Byoung-Tak Zhang

Object slip perception is essential for mobile manipulation robots to perform manipulation tasks reliably in the dynamic real-world. Traditional approaches to robot arms’ slip perception use tactile or vision sensors. However, mobile robots still have to deal with noise in their sensor signals caused by the robot’s movement in a changing environment. To solve this problem, we present an anomaly detection method that utilizes multisensory data based on a deep autoencoder model. The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor. The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status. Anomalies can then be identified by error scores measured by the difference between the trained encoder’s latent values and the latent values of reconstructed input data. In order to evaluate the proposed framework, we conducted an experiment that mimics an object slip by a mobile service robot operating in a real-world environment with diverse household objects and different moving patterns. The experimental results verified that the proposed framework reliably detects anomalies in object slip situations despite various object types and robot behaviors, and visual and auditory noise in the environment.

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AAAI Conference 2018 System Paper

Perception-Action-Learning System for Mobile Social-Service Robots Using Deep Learning

Beom-Jin Lee
Jinyoung Choi
Chung-Yeon Lee
Kyung-Wha Park
Sungjun Choi
Cheolho Han
Dong-Sig Han
Christina Baek

We introduce a robust integrated perception-action-learning system for mobile social-service robots. The state-of-the-art deep learning techniques were incorporated into each module which signiﬁcantly improves the performance in solving social service tasks. The system not only demonstrated fast and robust performance in a homelike environment but also achieved the highest score in the RoboCup2017@Home Social Standard Platform League (SSPL) held in Nagoya, Japan.

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IJCAI Conference 2016 Conference Paper

Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors

Sang-Woo Lee
Chung-Yeon Lee
Dong Hyun Kwak
Jiwon Kim
Jeonghee Kim
Byoung-Tak Zhang

Learning from human behaviors in the real world is important for building human-aware intelligent systems such as personalized digital assistants and autonomous humanoid robots. Everyday activities of human life can now be measured through wearable sensors. However, innovations are required to learn these sensory data in an online incremental manner over an extended period of time. Here we propose a dual memory architecture that processes slow-changing global patterns as well as keeps track of fast-changing local behaviors over a lifetime. The lifelong learnability is achieved by developing new techniques, such as weight transfer and an online learning algorithm with incremental features. The proposed model outperformed other comparable methods on two real-life data-sets: the image-stream dataset and the real-world lifelogs collected through the Google Glass for 46 days.

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