加速度ベースの報酬でヒューマノイド歩行を数分で学習するSTRIDE
STRIDE: Learning Humanoid Locomotion in Minutes with Acceleration-Based Rewards
機体のIMUだけで得られる加速度指令への追従を報酬とし、オフポリシーRLと左右対称拡張を組み合わせて、ヒューマノイドの歩行を約14分のサンプルで学習させる手法を提案した。
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2. 先行研究と比べてどこがすごい？
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著者: Nico Bohlinger, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
Learning locomotion directly on a humanoid robot is a promising direction to avoid the sim-to-real gap. However, locomotion rewards in Reinforcement Learning (RL) typically track the base velocity, which cannot be measured onboard without an external motion capture system or well-calibrated state estimator. This is why we introduce \method{}, which learns humanoid locomotion from minutes of samples with a reward that only requires the onboard Inertial Measurement Unit (IMU). Instead of tracking the base velocity, STRIDE rewards the robot for following a commanded acceleration profile. To learn efficiently from this reward, we use the off-policy RL algorithm XQC and combine a high gain setting for the joint position controllers with left-right symmetry augmentation. In simulation, STRIDE learns to walk on the Unitree G1 and Booster T1 humanoids within 14 minutes of samples. Compared to learning without these two components, it reduces the falls during training by 81%, while without them the robot learns only in over an hour.