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.

翻译：在高度杂乱和动态环境中生成可靠的局部规划方案，长期以来一直是阻碍实际应用的关键挑战。核心基础瓶颈包括：获取跨多样化场景的大规模专家演示数据，以及在有限数据下提升学习效率。本文提出SanD-Planner，一种基于扩散模型的样本高效局部规划器，它在钳位B样条空间内执行基于深度图像的模仿学习。通过在此紧凑空间内操作，所提算法本质上能产生平滑的输出，并在局部支撑域上具有有界的预测误差，自然地契合滚动时域执行方式。集成基于ESDF的安全检查器（包含显式安全裕度和完成时间度量）进一步减轻了与可行性评估相关的价值函数学习的训练负担。实验表明，仅使用$500$条演示轨迹（仅为基线方法所用演示数据规模的$0.25\%$）进行训练，SanD-Planner在所评估的开放基准测试中达到了最先进的性能，在模拟杂乱环境中成功率达到$90.1\%$，在室内模拟中达到$72.0\%$。通过展示其在2D和3D场景中向真实实验的零样本可迁移性，进一步证明了其性能。数据集与预训练模型也将开源。