PhySA: Decoupling Aesthetic and Physical Rewards for Humanoid Locomotion
Overview
This paper introduces PhySA, a method that separates aesthetic scoring from physical smoothness objectives in humanoid locomotion policies. By using vision-language models for aesthetic rewards and adding a phase-triggered correction channel for physical penalties, the approach reduces joint acceleration by 26-32% on MuJoCo Humanoid-v5 compared to baseline methods like PPO, Text2Reward, and Eureka, though it results in shorter episodes. The method shows limited transferability to other simulators or hardware without rescaling.
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Reported quantity · training steps: 2600000 other · Basis not reported
“mulator state. Ablations attribute the smoothness gain to the correction channel. On MuJoCo Humanoid-v5 at 2.6M steps, PhySA lowers joint acceleration by 26 to 32% relative to vanilla PPO, Text2Reward, and Eureka (three seeds), at the cost of shorter episodes. In a single extended run it lowers joint acceleration by 59.5% relative to Prompt2Policy, while a three-seed replication shows a smaller, non-significant reduc”
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- arXiv Robotics — research abstractsSignalPhySA: Decoupling Aesthetic and Physical Rewards for Humanoid Locomotion
This paper introduces PhySA, a method that separates aesthetic scoring from physical smoothness objectives in humanoid locomotion policies. By using vision-language models for aesthetic rewards and adding a phase-triggered correction channel for physical penalties, the approach reduces joint acceleration by 26-32% on MuJoCo Humanoid-v5 compared to baseline methods like PPO, Text2Reward, and Eureka, though it results in shorter episodes. The method shows limited transferability to other simulators or hardware without rescaling.
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