美的評価と物理的滑らかさを分離するヒューマノイド歩行学習
PhySA: Decoupling Aesthetic and Physical Rewards for Humanoid Locomotion
VLMによる美的スコアと物理的滑らかさが逆方向を向くことを示し、両者を独立した報酬チャネルに分離するPhySAを提案。MuJoCoで関節加速度を削減したが、他環境への転移は限定的。
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著者: Yunwan Fu, Sicheng He, Jiageng Mao, Yue Wang
分類: cs.RO
原文アブストラクト
Humanoid locomotion policies trained without an explicit smoothness objective tend to produce jerky, high-energy gaits that stress actuators. Vision-language models (VLMs) offer an appealing reward source: given a rendered rollout, a VLM returns an aesthetic quality score on [0,10] without task-specific feature engineering. We find, however, that aesthetic preference and physical smoothness point in opposite directions: across four production VLMs, clips with higher joint acceleration receive higher aesthetic scores (mean Pearson r=+0.66). We study PhySA (Physical Smoothness and Aesthetics), which keeps the two on separate, independently removable reward channels: a sparse, asynchronous VLM score and a phase-triggered correction channel that adjusts physical penalty weights from simulator 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 reduction (7.2%). The gain does not transfer to IsaacLab or the Unitree G1, even after rescaling the smoothness term.
