SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts

• URL: http://arxiv.org/abs/2606.03512v1
• Date: Tue, 02 Jun 2026 11:29:00 GMT
• Title: SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts
• Abstract summary: Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations.<n>These approaches face two key limitations: limited generalization to unseen environments and low robustness in demonstration collection.<n>This work introduces an enhanced framework that focuses on two main contributions: an overhauled annotation tool built on ROS 2, and a novel training strategy that integrates diffusion-based augmentation into baseline behavioral cloning models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Path planning is essential for Autonomous Mobile Robots (AMRs). Conventional methods for incorporating human preferences into planning typically rely on either complex reward engineering or hardware-intensive solutions. Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations. However, these approaches face two key limitations: limited generalization to unseen environments and low robustness in demonstration collection. To address these challenges, this work introduces an enhanced framework that focuses on two main contributions: an overhauled annotation tool built on ROS 2, and a novel training strategy that integrates diffusion-based augmentation into baseline behavioral cloning models. A dataset of expert demonstrations is provided and evaluated through ablation studies to assess the robustness of the proposed solution. The enhanced approach outperforms state-of-the-art methods with 39.1% lower Absolute Pose Error (APE) and 33.5% lower Fr'echet Inception Distance (FID) while having 93.8% less trainable parameters. Moreover it attains diffusion-level generalization while preserving the real-time, on-edge properties of state-of-the-art models.

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