  Diffusion planners have shown promise in handling long-horizon and
sparse-reward tasks due to the non-autoregressive plan generation. However,
their inherent stochastic risk of generating infeasible trajectories presents
significant challenges to their reliability and stability. We introduce a novel
approach, the Trajectory Aggregation Tree (TAT), to address this issue in
diffusion planners. Compared to prior methods that rely solely on raw
trajectory predictions, TAT aggregates information from both historical and
current trajectories, forming a dynamic tree-like structure. Each trajectory is
conceptualized as a branch and individual states as nodes. As the structure
evolves with the integration of new trajectories, unreliable states are
marginalized, and the most impactful nodes are prioritized for decision-making.
TAT can be deployed without modifying the original training and sampling
pipelines of diffusion planners, making it a training-free, ready-to-deploy
solution. We provide both theoretical analysis and empirical evidence to
support TAT's effectiveness. Our results highlight its remarkable ability to
resist the risk from unreliable trajectories, guarantee the performance
boosting of diffusion planners in $100\%$ of tasks, and exhibit an appreciable
tolerance margin for sample quality, thereby enabling planning with a more than
$3\times$ acceleration.
