From the 1 of 6 linked papers with an AI index.
6 papers
Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song +3
The paper presents APT-RL, a transformer‑based reinforcement learning framework that learns multiple locomotion skills from simulated data and enables a quadrupedal robot to traver…
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…
Contact-Implicit Model Predictive Control: Controlling Diverse Quadruped Motions Without Pre-Planned Contact Modes or Trajectories
Gijeong Kim, Dongyun Kang, Joon-Ha Kim +2
This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or f…
Tailoring Solution Accuracy for Fast Whole-body Model Predictive Control of Legged Robots
Charles Khazoom, Seungwoo Hong, Matthew Chignoli +2
Thanks to recent advancements in accelerating non-linear model predictive control (NMPC), it is now feasible to deploy whole-body NMPC at real-time rates for humanoid robots. Howev…
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…
Integrating Model-Based Footstep Planning with Model-Free Reinforcement Learning for Dynamic Legged Locomotion
Ho Jae Lee, Seungwoo Hong, Sangbae Kim
In this work, we introduce a control framework that combines model-based footstep planning with Reinforcement Learning (RL), leveraging desired footstep patterns derived from the L…