24 citations · 30 across the 5 of their papers we have counts for
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…
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…
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…
Proprioceptive External Torque Learning for Floating Base Robot and its Applications to Humanoid Locomotion
Daegyu Lim, Myeong-Ju Kim, Junhyeok Cha +2
The estimation of external joint torque and contact wrench is essential for achieving stable locomotion of humanoids and safety-oriented robots. Although the contact wrench on the…
Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
Donghyeon Kim, Glen Berseth, Mathew Schwartz +1
In this paper, we review the question of which action space is best suited for controlling a real biped robot in combination with Sim2Real training. Position control has been popul…