1 citations · 1 across the 4 of their papers we have counts for
6 papers
WarpMPC: Large-Batch MPC on GPU via ADMM with Unrolled Factorization
Henrik Hose, Se Hwan Jeon, Charles Khazoom +2
This paper introduces numerical optimizations for maximizing throughput on GPU when solving large batches (10,000 to over 100,000) of sequential quadratic programming (SQP) iterati…
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…
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…
CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control
Se Hwan Jeon, Seungwoo Hong, Ho Jae Lee +2
The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into thi…
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
Chenhao Li, Elijah Stanger-Jones, Steve Heim +1
Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage. To address thi…
Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback
Jenny Zhang, Steve Heim, Se Hwan Jeon +1
We present a minimal phase oscillator model for learning quadrupedal locomotion. Each of the four oscillators is coupled only to itself and its corresponding leg through local feed…