206 citations · 535 across the 89 of their papers we have counts for
6 papers · 1 filter
Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness
Haowen Liu, Xirui Li, Shaoxiong Yao +5
Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through embodied tools use offers a promising…
: 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…
DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation
Jusuk Lee, Seungjae Lee, Jonghun Shin +6
Robot manipulation critically depends on perception that preserves the action-relevant aspects of a scene. Yet most robot learning pipelines are built upon visual encoders pre-trai…
SIMPACT: Simulation-Enabled Action Planning using Vision-Language Models
Haowen Liu, Shaoxiong Yao, Haonan Chen +4
Vision-Language Models (VLMs) exhibit remarkable common-sense and semantic reasoning capabilities. However, they lack a grounded understanding of physical dynamics. This limitation…
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…