Title: Example-based Planning via Dual Gradient Fields. Abstract: Path planning is one of the key abilities of an intelligent agent. However, both the learning-based and sample-based planners remain to require explicitly defining the task by manually designing the reward function or optimisation objectives, which limits the scope of implementation. Formulating the path planning problem from a new perspective, Example-based planning is to find the most efficient path to increase the likelihood of the target distribution by giving a set of target examples. In this work, we introduce Dual Gradient Fields (DualGFs), an offline-learning example-based planning framework built upon score matching. There are two gradient fields in DualGFs: a target gradient field that guides task completion and a support gradient field that ensures moving with environmental constraints. In the learning process, instead of interacting with the environment, the agents are trained with two offline examples, i.e., the target gradients and support gradients are trained by target examples and support examples, respectively. The support examples are randomly sampled from free space, e.g., states without collisions. DualGF is a weighted mixture of the two fields, combining the merits of the two fields together. To update the mixing ratio adaptively, we further propose a fields-balancing mechanism based on Lagrangian-Relaxation. Experimental results across four tasks (navigation, tracking, particle rearrangement, and room rearrangement) demonstrate the scalability and effectiveness of our method.