no code implementations • 15 Mar 2024 • Jeongeun Park, Taemoon Jeong, Hyeonseong Kim, Taehyun Byun, Seungyoon Shin, Keunjun Choi, Jaewoon Kwon, Taeyoon Lee, Matthew Pan, Sungjoon Choi
This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas.
no code implementations • 13 Nov 2023 • Seunggyoon Shin, Seunggyu Chang, Sungjoon Choi
To be specific, inspired by human cognitive processes, the proposed method enables LLMs to utilize previous programming and debugging experiences to enhance the Python code completion tasks.
no code implementations • 25 Sep 2023 • Joonhyung Lee, Sangbeom Park, Jeongeun Park, Kyungjae Lee, Sungjoon Choi
Particularly, we focus on two aspects of the place task: stability robustness and contextual reasonableness of object placements.
1 code implementation • 17 Jun 2023 • Jeongeun Park, Seungwon Lim, Joonhyung Lee, Sangbeom Park, Minsuk Chang, Youngjae Yu, Sungjoon Choi
In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs).
no code implementations • 10 Feb 2023 • Jeongeun Park, Jefferson Silveria, Matthew Pan, Sungjoon Choi
In this paper, we propose a SOCratic model for Robots Approaching humans based on TExt System (SOCRATES) focusing on the human search and approach based on free-form textual description; the robot first searches for the target user, then the robot proceeds to approach in a human-friendly manner.
no code implementations • 27 Oct 2022 • Jihoon Kim, Youngjae Yu, Seungyoun Shin, Taehyun Byun, Sungjoon Choi
In this work, we present MoLang (a Motion-Language connecting model) for learning joint representation of human motion and language, leveraging both unpaired and paired datasets of motion and language modalities.
1 code implementation • 19 Sep 2022 • Jeongeun Park, Taerim Yoon, Jejoon Hong, Youngjae Yu, Matthew Pan, Sungjoon Choi
In this paper, we focus on the problem of efficiently locating a target object described with free-form language using a mobile robot equipped with vision sensors (e. g., an RGBD camera).
1 code implementation • 1 Sep 2022 • Jihoon Kim, Jiseob Kim, Sungjoon Choi
FLAME involves a new transformer-based architecture we devise to better handle motion data, which is found to be crucial to manage variable-length motions and well attend to free-form text.
1 code implementation • 9 Feb 2022 • Jihoon Kim, Taehyun Byun, Seungyoun Shin, Jungdam Won, Sungjoon Choi
Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking.
1 code implementation • 2 Nov 2021 • Jeongeun Park, Seungyoun Shin, Sangheum Hwang, Sungjoon Choi
Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a priori.
no code implementations • 27 Sep 2021 • Sangbeom Park, Yoonbyung Chai, Sunghyun Park, Jeongeun Park, Kyungjae Lee, Sungjoon Choi
In this paper, we present a semi-autonomous teleoperation framework for a pick-and-place task using an RGB-D sensor.
no code implementations • 11 Mar 2021 • Sungjoon Choi, Min Jae Song, Hyemin Ahn, Joohyung Kim
In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos.
no code implementations • 16 Dec 2020 • Mauricio Delbracio, Ignacio Garcia-Dorado, Sungjoon Choi, Damien Kelly, Peyman Milanfar
The proposed method estimates and removes mild blur from a 12MP image on a modern mobile phone in a fraction of a second.
no code implementations • 31 Jan 2019 • Kyungjae Lee, Sungyub Kim, Sungbin Lim, Sungjoon Choi, Songhwai Oh
By controlling the entropic index, we can generate various types of entropy, including the SG entropy, and a different entropy results in a different class of the optimal policy in Tsallis MDPs.
no code implementations • 27 Sep 2018 • Sungjoon Choi, Sanghoon Hong, Kyungjae Lee, Sungbin Lim
To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data.
2 code implementations • 28 May 2018 • Hyemin Ahn, Sungjoon Choi, Nuri Kim, Geonho Cha, Songhwai Oh
To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated.
no code implementations • NeurIPS 2018 • Kyungjae Lee, Sungjoon Choi, Songhwai Oh
Third, we propose a maximum causal Tsallis entropy imitation learning (MCTEIL) algorithm with a sparse mixture density network (sparse MDN) by modeling mixture weights using a sparsemax distribution.
1 code implementation • CVPR 2020 • Sungjoon Choi, Sanghoon Hong, Kyungjae Lee, Sungbin Lim
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 from a mixture of a target and correlated noise distributions. Our proposed method simultaneously estimates the target distribution and the quality of each data which is defined as the correlation between the target and data generating distributions. The cornerstone of the proposed method is a Cholesky Block that enables modeling dependencies among mixture distributions in a differentiable manner where we maintain the distribution over the network weights. We first provide illustrative examples in both regression and classification tasks to show the effectiveness of the proposed method. Then, the proposed method is extensively evaluated in a number of experiments where we show that it constantly shows comparable or superior performances compared to existing baseline methods in the handling of noisy data.
no code implementations • 16 Feb 2018 • Sungjoon Choi, John Isidoro, Pascal Getreuer, Peyman Milanfar
Denoising is a fundamental imaging problem.
no code implementations • 29 Nov 2017 • Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, Peyman Milanfar
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters.
no code implementations • 19 Sep 2017 • Kyungjae Lee, Sungjoon Choi, Songhwai Oh
The proposed sparse MDP is compared to soft MDPs which utilize causal entropy regularization.
1 code implementation • 3 Sep 2017 • Sungjoon Choi, Kyungjae Lee, Sungbin Lim, Songhwai Oh
The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.
1 code implementation • ECCV 2018 • Donghoon Lee, Sangdoo Yun, Sungjoon Choi, Hwiyeon Yoo, Ming-Hsuan Yang, Songhwai Oh
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior.
no code implementations • 12 Aug 2016 • Sungjoon Choi, Kyungjae Lee, Andy Park, Songhwai Oh
The performance of KDMRL is extensively evaluated in two sets of experiments: grid world and track driving experiments.
2 code implementations • 8 Feb 2016 • Sungjoon Choi, Qian-Yi Zhou, Stephen Miller, Vladlen Koltun
We have created a dataset of more than ten thousand 3D scans of real objects.
no code implementations • CVPR 2015 • Sungjoon Choi, Qian-Yi Zhou, Vladlen Koltun
We present an approach to indoor scene reconstruction from RGB-D video.