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Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding Cover

Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding

Open Access
|Aug 2025

References

  1. Belter D. Efficient modeling and evaluation of constraints in path planning for multi-legged walking robots. IEEE Access, 7:107845–107862, 2019.
  2. Das N. and Yip M. Learning-based proxy collision detection for robot motion planning applications. IEEE Transactions on Robotics, 36(4):1096–1114, 2020.
  3. Das N. and Yip M. C. Forward kinematics kernel for improved proxy collision checking. IEEE Robotics and Automation Letters, 5(2):2349–2356, 2020.
  4. Kennedy J. and Eberhart R. C. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, pages 1942–1948, 1995.
  5. Kicki P., Liu P., Tateo D., Bou-Ammar H., Walas K., Skrzypczyński P., and Peters J. Fast kinodynamic planning on the constraint manifold with deep neural networks. IEEE Transactions on Robotics, 40:277–297, 2024.
  6. Koptev M., Figueroa N., and Billard A. Neural joint space implicit signed distance functions for reactive robot manipulator control. IEEE Robotics and Automation Letters, 8(2):480–487, 2022.
  7. Krawczyk A., Marciniak J., and Belter D. Comparison of machine learning techniques for self-collisions checking of manipulating robots. In 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR), pages 472–477, 2023.
  8. Kulecki B. and Belter D. Boosting machine learning techniques with positional encoding for robot collision checking,. In 13th International Workshop on Robot Motion and Control (RoMoCo), pages 90–95, 2024.
  9. Kulecki B. and Belter D. Positional encoding for robot neural self-collision checking. In Proceedings of the 5th Polish Conference on Artificial Intelligence, 2024.
  10. Meister D., Ogaki S., Benthin C., Doyle M. J., Guthe M., and Bittner J. A survey on bounding volume hierarchies for ray tracing. Computer Graphics Forum, 40(2):683–712, 2023.
  11. Mildenhall B., Srinivasan P. P., Tancik M., Barron J. T., Ramamoorthi R., and Ng R. NeRF: Representing scenes as neural radiance fields for view synthesis. In Vedaldi A., Bischof H., Brox T., and Frahm J.-M., editors, Computer Vision – ECCV 2020, pages 405–421, Cham, 2020. Springer International Publishing.
  12. Montaut L., Lidec Q. L., Bambade A., Petrik V., Sivic J., and Carpentier J. Differentiable collision detection: a randomized smoothing approach. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3240–3246, 2023.
  13. Müller T., Evans A., Schied C., and Keller A. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1–102:15, July 2022.
  14. Pan J., Chitta S., and Manocha D. FCL: A general purpose library for collision and proximity queries. In 2012 IEEE International Conference on Robotics and Automation, pages 3859–3866, 2012.
  15. Park J. J., Florence P., Straub J., Newcombe R., and Lovegrove S. DeepSDF: Learning continuous signed distance functions for shape representation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 165–174, 2019.
  16. Park J. S. and Manocha D. Efficient probabilistic collision detection for non-gaussian noise distributions. IEEE Robotics and Automation Letters, 5(2):1024–1031, 2020.
  17. Press O., Smith N., and Lewis M. Train short, test long: Attention with linear biases enables input length extrapolation. In International Conference on Learning Representations, 2022.
  18. Raffel C., Shazeer N., Roberts A., Lee K., Narang S., Matena M., Zhou Y., Li W., and Liu P. J. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(1), Jan. 2020.
  19. Rahaman N., Baratin A., Arpit D., Dr¨axler F., Lin M., Hamprecht F. A., Bengio Y., and Courville A. C. On the spectral bias of neural networks. In International Conference on Machine Learning, 2018.
  20. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386–408, 1958.
  21. Rosinol A., Leonard J. J., and Carlone L. NeRF-SLAM: Real-time dense monocular slam with neural radiance fields. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3437–3444, 2023.
  22. Su J., Ahmed M., Lu Y., Pan S., Bo W., and Liu Y. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024.
  23. Tancik M., Srinivasan P. P., Mildenhall B., Fridovich-Keil S., Raghavan N., Sing-hal U., Ramamoorthi R., Barron J. T., and Ng R. Fourier features let networks learn high frequency functions in low dimensional domains. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS’20, Red Hook, NY, USA, 2020. Curran Associates Inc.
  24. Tracy K., Howell T. A., and Manchester Z. Differentiable collision detection for a set of convex primitives. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3663–3670, 2023.
  25. Valouch D. and Faigl J. Caterpillar heuristic for gait-free planning with multi-legged robot. IEEE Robotics and Automation Letters, 8(8):5204–5211, 2023.
  26. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., and Polosukhin I. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY, USA, 2017. Curran Associates Inc.
DOI: https://doi.org/10.2478/fcds-2025-0015 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 383 - 402
Submitted on: Nov 28, 2024
Accepted on: Jun 17, 2025
Published on: Aug 21, 2025
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Bartłomiej Kulecki, Dominik Belter, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.