6 citations · 12 across the 14 of their papers we have counts for
14 papers
Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time
Jeongeun Park, Juhan Park, Taekyung Kim +3
Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in bot…
LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
Jihwan Yoon, Taemoon Jeong, Jeongeun Park +5
Designing robot morphologies and kinematics has traditionally relied on human intuition, with little systematic foundation. Motion-design co-optimization offers a promising path to…
Learning Dexterous Grasping from Sparse Taxonomy Guidance
Juhan Park, Taerim Yoon, Seungmin Kim +10
Dexterous manipulation requires planning a grasp configuration suited to the object and task, which is then executed through coordinated multi-finger control. However, specifying g…
Hierarchical Vision Language Action Model Using Success and Failure Demonstrations
Jeongeun Park, Jihwan Yoon, Byungwoo Jeon +6
Prior Vision-Language-Action (VLA) models are typically trained on teleoperated successful demonstrations, while discarding numerous failed attempts that occur naturally during dat…
A Unified Framework for Motion Reasoning and Generation in Human Interaction
Jeongeun Park, Sungjoon Choi, Sangdoo Yun
Recent advancements in large language models (LLMs) have significantly improved their ability to generate natural and contextually relevant text, enabling more human-like AI intera…
Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematics (MASK)
Jeongeun Park, Taemoon Jeong, Hyeonseong Kim +7
This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations…
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…
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation
Jeongeun Park, Seungyoun Shin, Sangheum Hwang +1
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. Our proposed…
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…