Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917421400457216 |
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| author | Kulecki, Bartłomiej Belter, Dominik |
| author_facet | Kulecki, Bartłomiej Belter, Dominik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding Kulecki, Bartłomiej Belter, Dominik Robotics 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. |
| title | Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.07542 |