让轨迹规划自动决定走多远，更智能也更稳定。
VH-Diffuser: Variable Horizon Diffusion Planner for Time-Aware Goal-Conditioned Trajectory Planning
- 用学习模型预测每条任务的最优轨迹长度
- 在迷宫和机械臂任务中成功率更高，路径更高效
- 无需改架构，适合想提升规划鲁棒性的研究者
基于扩散模型的规划器在长时序任务中表现优异，但现有方法通常采用固定预设的规划时长，导致轨迹过短或过长，且对不同难度任务适应性差。本文提出可变时长扩散规划器（VHD），将规划时长作为可学习变量。给定起终点后，先通过学习的长度预测模型估计该任务所需的具体时长，再引导扩散规划器生成相应长度的轨迹。该设计通过初始噪声调节和随机截取子轨迹训练实现长度控制，无需修改模型结构。实验表明，VHD在迷宫导航和机械臂控制任务中显著提升成功率与路径效率，对时长不匹配和未见长度更具鲁棒性，同时保持训练简单且完全离线。
原文摘要 · Abstract (English)
Diffusion-based planners have gained significant recent attention for their robustness and performance in long-horizon tasks. However, most existing planners rely on a fixed, pre-specified horizon during both training and inference. This rigidity often produces length-mismatch (trajectories that are too short or too long) and brittle performance across instances with varying geometric or dynamical difficulty. In this paper, we introduce the Variable Horizon Diffuser (VHD) framework, which treats the horizon as a learned variable rather than a fixed hyperparameter. Given a start-goal pair, we first predict an instance-specific horizon using a learned Length Predictor model, which guides a Diffusion Planner to generate a trajectory of the desired length. Our design maintains compatibility with existing diffusion planners by controlling trajectory length through initial noise shaping and training on randomly cropped sub-trajectories, without requiring architectural changes. Empirically, VHD improves success rates and path efficiency in maze-navigation and robot-arm control benchmarks, showing greater robustness to horizon mismatch and unseen lengths, while keeping training simple and offline-only.
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