114 citations · 195 across the 22 of their papers we have counts for
12 papers · 1 filter
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…
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents
Jeongeun Park, Seungwon Lim, Joonhyung Lee +4
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…
SOCRATES: Text-based Human Search and Approach using a Robot Dog
Jeongeun Park, Jefferson Silveria, Matthew Pan +1
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 descri…
Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants
Jeongeun Park, Taerim Yoon, Jejoon Hong +3
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 ca…
Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving
Gunmin Lee, Wooseok Oh, Seungyoun Shin +5
In this paper, we consider the problem of autonomous driving using imitation learning in a semi-supervised manner. In particular, both labeled and unlabeled demonstrations are leve…
Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities
Jeongwoo Oh, Gunmin Lee, Jeongeun Park +8
Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in auto…
Learning Robot Structure and Motion Embeddings using Graph Neural Networks
J. Taery Kim, Jeongeun Park, Sungjoon Choi +1
We propose a learning framework to find the representation of a robot's kinematic structure and motion embedding spaces using graph neural networks (GNN). Finding a compact and low…
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…
Self-Supervised Motion Retargeting with Safety Guarantee
Sungjoon Choi, Min Jae Song, Hyemin Ahn +1
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…
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
Hyemin Ahn, Sungjoon Choi, Nuri Kim +2
In this paper, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration which enables an effective interaction with a human user despite the ambiguity in…
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…
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 (…