Tree-guided Diffusion Planner
Hyeonseong Jeon, Cheolhong Min, Jaesik Park
Planning with pretrained diffusion models has emerged as a promising approach for solving test-time guided control problems. However, standard gradient-based guidance typically performs optimally under convex and differentiable reward landscapes, showing substantially reduced effectiveness in real-world scenarios involving non-convex objectives, non-differentiable constraints, and multi-reward structures. We propose a Tree-guided Diffusion Planner (TDP), a flexible test-time planning framework that balances exploration and exploitation through structured trajectory generation. We build a trajectory tree using a two-phase denoising process: (1) diverse parent trajectories are produced via fixed-potential particle guidance to encourage broad exploration, and (2) sub-trajectories are locally refined through fast conditional denoising guided by task objectives. Our bi-level sampling framework offers a robust, training-free solution to test-time planning, addressing the limitations of gradient-based guidance by exploring diverse trajectory regions and harnessing gradient information across this expanded solution space.We evaluate our approach on three tasks, including Maze2D gold-picking, robot arm block manipulation, and AntMaze multi-goal exploration. Our approach consistently outperforms state-of-the-art planning approaches across all three tasks, which consist of a wide range of guidance functions.