1 citations · 1 across the 7 of their papers we have counts for
8 papers
What to Attend, What to Keep: Skill-Conditioned Visuotactile Representation with Progress-Guided Event Memory
Amir-Hossein Shahidzadeh, Seungjae Lee, Eadom Dessalene +5
Robotic manipulation integrates vision, touch, and language, whose importance shifts across stages: vision guides reaching, while touch, through its evolution over time, decides gr…
Beyond Episodic Evaluation: Memory Architectural Bottlenecks in Sequential Embodied Question Answering
Zikui Cai, Kaushal Janga, Tan Dat Dao +15
Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes. Ho…
: A Scalable 3D Interaction-Trace World Model
Seungjae Lee, Yoonkyo Jung, Jusuk Lee +6
World models that capture how actions induce physical change enable scalable robot learning without reliance on embodiment-specific action labels. Pixel-space video models provide…
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
Zhi Wang, Botao He, Kelin Yu +4
Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challenging due to the embodiment gap b…
MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning
Yuanchen Ju, Yongyuan Liang, Yen-Jen Wang +7
Mobile manipulators in households must both navigate and manipulate. This requires a compact, semantically rich scene representation that captures where objects are, how they funct…
TraceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos
Seungjae Lee, Yoonkyo Jung, Inkook Chun +8
Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments - humans and different robo…
Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models
Seungjae Lee, Daniel Ekpo, Haowen Liu +3
Exploration is essential for general-purpose robotic learning, especially in open-ended environments where dense rewards, explicit goals, or task-specific supervision are scarce. V…
Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis
Jeffrey Yang Fan Chiang, Seungjae Lee, Jia-Bin Huang +2
Recent advancements in Web AI agents have demonstrated remarkable capabilities in addressing complex web navigation tasks. However, emerging research shows that these agents exhibi…