Robotics: Science and Systems XXII

SanD-Planner: Sample-Efficient Diffusion Planner in B-Spline Space for Robust Local Navigation

Jincheng Wang, Lingfan Bao, Tong Yang, Diego Martinez Plasencia, Jianhao Jiao, Dimitrios Kanoulas

Abstract:

The challenge of generating reliable local plans has long hindered practical applications in highly cluttered and dynamic environments. Key fundamental bottlenecks include acquiring large-scale expert demonstrations across diverse scenes and improving learning efficiency with limited data. This paper proposes SanD-Planner, a sample-efficient diffusion-based local planner that conducts depth image-based imitation learning within the clamped B-spline space. By operating within this compact space, the proposed algorithm inherently yields smooth outputs with bounded prediction errors over local supports, naturally aligning with receding-horizon execution. Integration of an ESDF-based safety checker with explicit clearance and time-to-completion metrics further reduces the training burden associated with value-function learning for feasibility assessment. Experiments show that training with 500 episodes (merely 0.25% of the demonstration scale used by the baseline), SanD-Planner achieves state-of-the-art performance on the evaluated open benchmark, attaining success rates of 90.1% in simulated cluttered environments and 72.0% in indoor simulations. The performance is further proven by demonstrating zero-shot transferability to realistic experimentation in both 2D and 3D scenes. The dataset and pre-trained models will also be open-sourced.

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Bibtex:

  
@INPROCEEDINGS{WangJ2-RSS-26, 
    AUTHOR    = {Jincheng Wang AND Lingfan Bao AND Tong Yang AND Diego Martinez Plasencia AND Jianhao Jiao AND Dimitrios Kanoulas}, 
    TITLE     = {{SanD-Planner: Sample-Efficient Diffusion Planner in B-Spline Space for Robust Local Navigation}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.069} 
}