Search Results for author: Sungjoon Choi

Found 26 papers, 10 papers with code

Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematics (MASK)

no code implementations15 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.

Past as a Guide: Leveraging Retrospective Learning for Python Code Completion

no code implementations13 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.

Code Completion

SPOTS: Stable Placement of Objects with Reasoning in Semi-Autonomous Teleoperation Systems

no code implementations25 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.

CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents

1 code implementation17 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).

Question Generation Uncertainty Quantification

SOCRATES: Text-based Human Search and Approach using a Robot Dog

no code implementations10 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.

Knowledge Distillation

Learning Joint Representation of Human Motion and Language

no code implementations27 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.

Action Recognition Contrastive Learning +2

Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants

1 code implementation19 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).

Object Robot Navigation

FLAME: Free-form Language-based Motion Synthesis & Editing

1 code implementation1 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.

motion prediction Motion Synthesis

Conditional Motion In-betweening

1 code implementation9 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.

Pose Prediction

Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation

1 code implementation2 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.

Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills

no code implementations27 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.

Self-Supervised Motion Retargeting with Safety Guarantee

no code implementations11 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.

motion retargeting Position +1

Polyblur: Removing mild blur by polynomial reblurring

no code implementations16 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.

Deblurring Super-Resolution

Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning

no code implementations31 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.

reinforcement-learning Reinforcement Learning (RL)

ChoiceNet: Robust Learning by Revealing Output Correlations

no code implementations27 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.

regression

Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration

2 code implementations28 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.

Object Position

Maximum Causal Tsallis Entropy Imitation Learning

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.

Imitation Learning

Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks

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.

Autonomous Driving General Classification +2

BLADE: Filter Learning for General Purpose Computational Photography

no code implementations29 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.

Demosaicking Denoising +1

Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling

1 code implementation3 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.

Autonomous Driving

Unsupervised Holistic Image Generation from Key Local Patches

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.

Image Generation

Density Matching Reward Learning

no code implementations12 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.

Autonomous Navigation

A Large Dataset of Object Scans

2 code implementations8 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.

Object

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