提出动作模式分解方法，实现机器人少样本跨形态迁移
MOTIF: Learning Action Motifs for Few-shot Cross-Embodiment Transfer
- 将动作分解为与形态无关的时空模式（动作模板）
- 实测在真实场景中比基线提升43.7%成功率
- 适合需快速适配新机器人的智能系统开发者
尽管视觉-语言-动作（VLA）模型推动了通用机器人学习的发展，但因运动学差异和收集足够真实演示数据成本过高，跨形态迁移仍具挑战。现有方法多依赖共享-私有架构，受限于私有参数容量且缺乏显式适应机制。为此，本文提出MOTIF，通过向量量化结合进度感知对齐与形态对抗约束，学习统一的动作模板（action motifs），并解耦异构动作数据中的时空模式。随后设计轻量级预测器，从实时输入中预测这些模板，融合机器人特定状态后驱动流匹配策略生成动作，实现对新形态的快速适配。在仿真与真实环境中的评估表明，MOTIF在少样本迁移场景中分别比强基线提升6.5%（仿真）与43.7%（真实）。代码已开源。
原文摘要 · Abstract (English)
While vision-language-action (VLA) models have advanced generalist robotic learning, cross-embodiment transfer remains challenging due to kinematic heterogeneity and the high cost of collecting sufficient real-world demonstrations to support fine-tuning. Existing cross-embodiment policies typically rely on shared-private architectures, which suffer from limited capacity of private parameters and lack explicit adaptation mechanisms. To address these limitations, we introduce MOTIF for efficient few-shot cross-embodiment transfer that decouples embodiment-agnostic spatiotemporal patterns, termed action motifs, from heterogeneous action data. Specifically, MOTIF first learns unified motifs via vector quantization with progress-aware alignment and embodiment adversarial constraints to ensure temporal and cross-embodiment consistency. We then design a lightweight predictor that predicts these motifs from real-time inputs to guide a flow-matching policy, fusing them with robot-specific states to enable action generation on new embodiments. Evaluations across both simulation and real-world environments validate the superiority of MOTIF, which significantly outperforms strong baselines in few-shot transfer scenarios by 6.5% in simulation and 43.7% in real-world settings. Code is available at https://github.com/buduz/MOTIF.
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