14 papers
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Donghu Kim, Youngdo Lee, Hojoon Lee +6
Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challeng…
See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models
Byungkun Lee, Dongyoon Hwang, Dongjin Kim +3
The paper proposes robot-centric pointmaps, which encode 3D scene coordinates in the robot's frame as image pixels, enabling vision‑language‑action models to align visual inputs wi…
3D HAMSTER: Bridging Planning and Control in Hierarchical Vision Language Action Models through 3D Trajectory Guidance
Dongyoon Hwang, Byungkun Lee, Dongjin Kim +7
Hierarchical Vision-Language-Action (VLA) models decouple high-level planning from low-level control to improve generalization in robot manipulation. Recent work in this paradigm u…
PHUMA: Physically Reliable Humanoid Locomotion Dataset
Kyungmin Lee, Sibeen Kim, Youngdo Lee +6
Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture dat…
RDA: Reward Design Agent for Reinforcement Learning
Hojoon Lee, Ajay Subramanian, Ben Abbatematteo +4
Reinforcement learning has enabled the acquisition of impressive robotic skills, but typically requires hand-crafted reward functions that are slow to design and difficult to align…
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
Donghu Kim, Youngdo Lee, Minho Park +10
Reinforcement learning (RL) is a core approach for robot control when expert demonstrations are unavailable. On-policy methods such as Proximal Policy Optimization (PPO) are widely…