軌道標準化によるロボット模倣学習の改善
Improving Robotic Imitation Learning via Trajectory Standardization
人間のデモンストレーション軌道の速度むらや冗長な停止を情報距離に基づいて再サンプリングする前処理手法ISRを提案し、実世界の操作タスクで成功率を約25%向上させた。
詳しい要約
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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: Licheng Yang, Lingfeng Qian, Fei Zheng, Yonghao He, Wei Sui, Shuangshuang Li, Hu Su
分類: cs.RO, cs.CV
原文アブストラクト
Imitation learning for robotic manipulation relies on large sets of human demonstration trajectories, which are often noisy and temporally irregular due to variable operator speed, intermittent pauses, and inconsistent action density. A common preprocessing strategy is time-uniform downsampling to shorten sequences, but it cannot effectively remove speed-induced non-uniformity or redundant pauses. This mismatch degrades data quality and hinders policy learning. To address this issue, we propose Information-Standardized Trajectory Resampling (ISR), an offline preprocessing method for effective imitation learning. ISR resamples each trajectory by enforcing approximately equal information distance between adjacent points. Specifically, we map trajectories onto an information-modulated Riemannian manifold and perform geodesic-equidistant parameterization. We construct an information-intensity field from velocity and acceleration norms: the velocity term removes small-motion redundancy, while the acceleration term preserves high-curvature and fine-manipulation phases. We evaluate ISR on three real-world manipulation tasks with mainstream imitation learning policies. Compared with the baseline time-uniform 3x downsampling, ISR improves task success rates by about 25%, remains robust across datasets collected from different operators, and reduces both dataset size and training cost. The code and videos are publicly available at https://d-robotics-ai-lab.github.io/isr.page.
