AI 中文总结
本文针对扩散引导的RRT运动规划，提出两种推理时多样化策略并通过H-Graph混合，在AntMaze任务中显著提升轨迹质量与长度指标。
AI 中文摘要
由扩散模型引导的基于采样的运动规划器能够在单次运行中生成高质量的轨迹，然而推理时可用的随机多样性在很大程度上未被利用。我们针对一个固定的、预训练的DiTree模型提出了两种推理时多样化策略，并通过H-Graph混合进行结合，在15个迷宫场景中对全向AntMaze机器人进行了评估。第一种策略为阶乘多样性，它扫描随机种子和扩散目标偏差（DGB）参数；第二种策略为仅细化多样性，它扫描控制RRT生成轨迹被编辑程度的扩散细化强度（RS）。由于单次运行的基线仅部分成功，我们另外将H-Graph结果与基于池的统计进行了比较。H-Graph分别将阶乘多样性和仅细化多样性的平均池长度提高了18.8%和14.5%。此外，它还将最佳单个候选轨迹的长度分别提高了9.7%和6.8%。最后，与成功基线的轨迹长度相比，它分别将结果提高了18.2%和19.9%。这些结果表明，推理时参数变化是一种可靠的、无需训练的路径多样性来源，而H-Graph混合能够可靠地将这种多样性转化为更短、更高质量的轨迹。
英文摘要
Sampling-based motion planners guided by diffusion models produce high-quality trajectories in a single run, yet the stochastic diversity available at inference time is left largely unexploited. We present two inference-time diversification strategies for a fixed, pretrained DiTree model, combined via H-Graph hybridization, and evaluate them on a holonomic AntMaze robot across 15 maze scenarios. The first, factorial diversity, sweeps the random seed and Diffusion Goal Bias (DGB) parameter, the second, refinement-only diversity, sweeps the diffusion refinement strength (RS) that controls how much an RRT-generated trajectory is edited. Because a single-run baseline only partially succeeds, we additionally compare H-Graph results with pool-based statistics. H-Graph improves the mean pool length of the factorial and refinement-only diversities by 18.8% and 14.5%, respectively. In addition, it also improves the best individual candidate's lengths by 9.7% and 6.8%, respectively. And last, compared with the successful baseline's trajectory length, it improves the results by 18.2% and 19.9%, respectively. These results show that inference-time parameter variation is a reliable, training-free source of path diversity, and that H-Graph hybridization reliably converts this diversity into shorter, higher quality trajectories.
