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…
TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards
Hokyun Lee, Woo-Jeong Baek, Junhyeok Cha +1
With the growing employment of learning algorithms in robotic applications, research on reinforcement learning for bipedal locomotion has become a central topic for humanoid roboti…
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…