49 citations · 95 across the 13 of their papers we have counts for
15 papers
Learning-based Dynamic Robot-to-Human Handover
Hyeonseong Kim, Chanwoo Kim, Matthew Pan +2
This paper presents a novel learning-based approach to dynamic robot-to-human handover, addressing the challenges of delivering objects to a moving receiver. We hypothesize that dy…
SPOTS: Stable Placement of Objects with Reasoning in Semi-Autonomous Teleoperation Systems
Joonhyung Lee, Sangbeom Park, Jeongeun Park +2
Pick-and-place is one of the fundamental tasks in robotics research. However, the attention has been mostly focused on the ``pick'' task, leaving the ``place'' task relatively unex…
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
Dohyeong Kim, Kyungjae Lee, Songhwai Oh
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints must be met, such as avoiding collisions, limiting energy consumption, and maintainin…
Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills
Sangbeom Park, Yoonbyung Chai, Sunghyun Park +3
In this paper, we present a semi-autonomous teleoperation framework for a pick-and-place task using an RGB-D sensor. In particular, we assume that the target object is located in a…
SWAD: Domain Generalization by Seeking Flat Minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee +4
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains. Although a variety of DG methods…
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
Kyungjae Lee, Hongjun Yang, Sungbin Lim +1
In this paper, we consider stochastic multi-armed bandits (MABs) with heavy-tailed rewards, whose -th moment is bounded by a constant for . First, we propose a…
Relational Deep Feature Learning for Heterogeneous Face Recognition
MyeongAh Cho, Taeoh Kim, Ig-Jae Kim +2
Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the la…
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
Joosung Lee, Sangwon Hwang, Kyungjae Lee +4
Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we p…
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
Kyungjae Lee, Sungyub Kim, Sungbin Lim +2
In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinfo…
Maximum Causal Tsallis Entropy Imitation Learning
Kyungjae Lee, Sungjoon Choi, Songhwai Oh
In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a sparse multi-modal policy distribution from d…
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks
Sungjoon Choi, Sanghoon Hong, Kyungjae Lee +1
In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems.We assume that the training outputs are collected fro…
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning
Kyungjae Lee, Sungjoon Choi, Songhwai Oh
In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and mu…
A Nonparametric Motion Flow Model for Human Robot Cooperation
Sungjoon Choi, Kyungjae Lee, H. Andy Park +1
In this paper, we present a novel nonparametric motion flow model that effectively describes a motion trajectory of a human and its application to human robot cooperation. To this…
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
Sungjoon Choi, Kyungjae Lee, Sungbin Lim +1
In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriat…
Density Matching Reward Learning
Sungjoon Choi, Kyungjae Lee, Andy Park +1
In this paper, we focus on the problem of inferring the underlying reward function of an expert given demonstrations, which is often referred to as inverse reinforcement learning (…