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About

Junhyeok Cha is a leading researcher in humanoid robotics and telepresence systems, whose work bridges the gap between simulation and real-world deployment. His primary research areas include bipedal locomotion, sim-to-real transfer for reinforcement learning, and intuitive human-robot interaction for avatar systems. Cha made significant contributions to the field by developing robust walking algorithms for humanoid robots, as demonstrated in his comprehensive review of walking technologies (6 citations), and by pioneering methods for proprioceptive external torque estimation that eliminate the need for costly force-torque sensors (6 citations). His work on MOB-Net (4 citations) further advanced sensorless torque estimation by addressing model uncertainty and friction. Cha’s impact is particularly evident in his participation in the ANA Avatar XPRIZE finals, where his team’s intuitive and interactive robotic avatar system (8 citations) showcased the potential for tele-existence. Additionally, his research on bridging the reality gap for reinforcement learning in bipedal locomotion (7 citations) has provided critical insights for training humanoid robots in simulation before real-world application. Through his innovative use of virtual reality for spatial visual interfaces (2 citations), Cha continues to push the boundaries of how humans interact with and control robotic avatars.