11 citations · 36 across the 12 of their papers we have counts for
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cs.GR2024★ 1 cited
Learning Human-like Locomotion Based on Biological Actuation and Rewards
Minkwan Kim, Yoonsang Lee
We propose a method of learning a policy for human-like locomotion via deep reinforcement learning based on a human anatomical model, muscle actuation, and biologically inspired re…
cs.GR2024★ 3 cited
Performance-Based Biped Control using a Consumer Depth Camera
Yoonsang Lee, Taesoo Kwon
We present a technique for controlling physically simulated characters using user inputs from an off-the-shelf depth camera. Our controller takes a real-time stream of user poses a…
cs.GR2020★ 2 cited
Understanding the Stability of Deep Control Policies for Biped Locomotion
Hwangpil Park, Ri Yu, Yoonsang Lee +2
Achieving stability and robustness is the primary goal of biped locomotion control. Recently, deep reinforce learning (DRL) has attracted great attention as a general methodology f…