让机器人根据用户偏好动态调整规划保守性，无需重新训练。
MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning
- 用条件化对比学习构建可调节的潜在表示空间
- 同一任务下按用户偏好生成从激进到保守的多种路径
- 适合需要灵活安全策略的机器人规划场景
时序对比表示学习能将长程规划简化为低维线性系统的推理。但现有方法仅学习单一潜在几何结构，无法区分相同起终点下效率与风险权衡的不同有效行为。本文提出MoMo，一种偏好条件化的对比规划方法，通过标量用户偏好在推理时连续调节规划保守性，无需重训练。MoMo利用特征逐维线性调制和低秩神经调制，联合学习表示几何与潜在预测算子的条件化。其形式保持了表示空间中所需的概率密度比，确保推理效率。在六个环境中，MoMo能根据用户偏好平滑调节计划安全性，在时间一致性和偏好一致性上优于状态增强基线方法。
原文摘要 · Abstract (English)
Temporally contrastive representation learning induces a latent structure capable of reducing long-horizon planning to inference in a low-dimensional linear system. However, existing contrastive planning work learns a single latent geometry which cannot distinguish multiple valid behaviors trading task efficiency against risk exposure for the same start-goal query. We introduce MoMo, a preference-conditioned contrastive planner allowing a scalar user preference to continuously modulate plan conservativeness at inference time, without retraining. MoMo learns a joint conditioning of the representation geometry and latent prediction operator via Feature-Wise Linear Modulation and low-rank neural modulation, respectively. We show that our formulation preserves the probability density ratio encoded in the representation space that is required for inference-driven contrastive planning, further retaining its inference-time efficiency. Across six environments, MoMo smoothly adapts plan safety according to user preferences, yielding improved temporal and preferential consistency over state augmentation baselines.
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