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End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile Robots

2025-03-21 · IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS) 2025 3 · Anthony Rizk, Charbel Abi Hana, Youssef Bakouny, Flavia Khatounian

Path planning is crucial for Autonomous Mobile Robots applications. Traditionally, path planning based on human input and preferences has relied on hard to define reward-based learning or costly techniques requiring additional hardware. This work introduces a more accessible and flexible approach through sketch-guided imitation learning, where nontechnical users can simply draw the desired navigational path on a provided 2D map, which is then used to teach U-net models path planning behaviors. Additionally, the work draws on metrics from the fields of image generation and robotics to provide a novel evaluation framework. The approach is integrated into an end-to-end robotics stack to demonstrate its usability. The dataset and code are provided on https://github.com/charbel-a-hC/SKIPP.

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Code (1)

charbel-a-hC/SKIPP pytorch

Tasks

Image GenerationImitation Learning

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ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…
Concatenated Skip Connection A Concatenated Skip Connection is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from…
Max Pooling Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually…
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
U-Net 설명 없음

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