3 papers
cs.RO2026
Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
Ho Jae Lee, Yonghyeon Lee, Alexander Alexiev +3
In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent from low-level joint execution.…
cs.RO2025
Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control
Se Hwan Jeon, Ho Jae Lee, Seungwoo Hong +1
Model Predictive Control (MPC) provides interpretable, tunable locomotion controllers grounded in physical models, but its robustness depends on frequent replanning and is limited…
cs.RO2025
Learning Humanoid Arm Motion via Centroidal Momentum Regularized Multi-Agent Reinforcement Learning
Ho Jae Lee, Se Hwan Jeon, Sangbae Kim
Humans naturally swing their arms during locomotion to regulate whole-body dynamics, reduce angular momentum, and help maintain balance. Inspired by this principle, we present a li…