Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
This manuscript investigates the integration of positional encoding – a technique widely used in computer graphics – into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classifi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2025-09-01
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| Series: | Foundations of Computing and Decision Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/fcds-2025-0015 |
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| Summary: | This manuscript investigates the integration of positional encoding – a technique widely used in computer graphics – into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models. |
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| ISSN: | 2300-3405 |