5 papers
Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
Junhyeok Rui Cha, Woohyun Cha, Jaeyong Shin +2
This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Unlike prior methods that typically rely on domain…
Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
Junhyeok Rui Cha, Woohyun Cha, Jaeyong Shin +2
This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Prior sim-to-real methods for legged robots mostly…
Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion
Jaeyong Shin, Woohyun Cha, Donghyeon Kim +2
Reinforcement learning (RL) has shown great potential in training agile and adaptable controllers for legged robots, enabling them to learn complex locomotion behaviors directly fr…
MOB-Net: Limb-modularized Uncertainty Torque Learning of Humanoids for Sensorless External Torque Estimation
Daegyu Lim, Myeong-Ju Kim, Junhyeok Cha +1
Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance o…
Efficient Computation of Whole-Body Control Utilizing Simplified Whole-Body Dynamics via Centroidal Dynamics
Junewhee Ahn, Jaesug Jung, Yisoo Lee +3
In this study, we present a novel method for enhancing the computational efficiency of whole-body control for humanoid robots, a challenge accentuated by their high degrees of free…