Abstract
We present a novel, hybrid neuro-genetic visuomotor architecture for object grasping on a humanoid robot. The approach combines the state-of-the-art object detector RetinaNet, a neural network-based coordinate transformation and a genetic-algorithm-based inverse kinematics solver. We claim that a hybrid neural architecture can utilise the advantages of neural and genetic approaches: while the neural components accurately locate objects in the robot’s three-dimensional reference frame, the genetic algorithm allows reliable motor control for the humanoid, despite its complex kinematics. The modular design enables independent training and evaluation of the components. We show that the additive error of the coordinate transformation and inverse kinematics solver is appropriate for a robotic grasping task. We additionally contribute a novel spatial-oversampling approach for training the neural coordinate transformation that overcomes the known issue of neural networks with extrapolation beyond training data and the extension of the genetic inverse kinematics solver with numerical fine-tuning. The grasping approach was realised and evaluated on the humanoid robot platform NICO in a simulation environment.
The authors gratefully acknowledge partial support from the German Research Foundation DFG under project CML (TRR 169).
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References
Aristidou, A., Lasenby, J.: Inverse kinematics: a review of existing techniques and introduction of a new fast iterative solver. Technical report. Cambridge University Engineering Department (2009)
Bergstra, J., Yamins, D., Cox, D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: 30th International Conference on Machine Learning (ICML 2013), pp. 115–123 (2013)
Daya, B., Khawandi, S., Akoum, M.: Applying neural network architecture for inverse kinematics problem in robotics. J. Softw. Eng. Appl. 3(03), 230 (2010)
Eppe, M., Kerzel, M., Griffiths, S., Ng, H.G., Wermter, S.: Combining deep learning for visuomotor coordination with object identification to realize a high-level interface for robot object-picking. In: IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 612–617 (2017)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jamone, L., Natale, L., Nori, F., Metta, G., Sandini, G.: Autonomous online learning of reaching behavior in a humanoid robot. Int. J. Humanoid Rob. 9(03), 1250017 (2012)
Kerzel, M., Eppe, M., Heinrich, S., Abawi, F., Wermter, S.: Neurocognitive shared visuomotor network for end-to-end learning of object identification, localization and grasping on a humanoid. In: Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), pp. 19–24, September 2019
Kerzel, M., Strahl, E., Magg, S., Navarro-Guerrero, N., Heinrich, S., Wermter, S.: NICO - Neuro-Inspired COmpanion: a developmental humanoid robot platform for multimodal interaction. In: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 113–120. IEEE (2017)
Darvishi Boloorani, A., Samany, N.N., Mirzaei, S., Bahrami, H.A., Alavipanah, S.K.: Remote sensing and GIS for dust storm studies in Iraq. In: Al-Quraishi, A.M.F., Negm, A.M. (eds.) Environmental Remote Sensing and GIS in Iraq. SW, pp. 333–375. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-21344-2_14
Köker, R.: A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Inf. Sci. 222, 528–543 (2013)
Leitner, J., Harding, S., Förster, A., Corke, P.: A modular software framework for eye-hand coordination in humanoid robots. Front. Robot. AI 3, 26 (2016)
Levine, S., Finn, C., Darrell, T., Abbeel, P.: End-to-end training of deep visuomotor policies. J. Mach. Learn. Res. 17(1), 1334–1373 (2016)
Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007 (2017)
Marsland, S.: Machine Learning: An Algorithmic Perspective, 2nd edn. Chapman & Hall/CRC, United States (2014)
Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., Levine, S.: Deep reinforcement learning for vision-based robotic grasping: a simulated comparative evaluation of off-policy methods. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6284–6291. IEEE (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Savastano, P., Nolfi, S.: A robotic model of reaching and grasping development. IEEE Trans. Auton. Mental Dev. 5(4), 326–336 (2013)
Starke, S., Hendrich, N., Magg, S., Zhang, J.: An efficient hybridization of genetic algorithms and particle swarm optimization for inverse kinematics. In: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1782–1789. IEEE (2016)
Trask, A., Hill, F., Reed, S.E., Rae, J., Dyer, C., Blunsom, P.: Neural arithmetic logic units. In: Advances in Neural Information Processing Systems, pp. 8035–8044 (2018)
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Kerzel, M., Spisak, J., Strahl, E., Wermter, S. (2020). Neuro-Genetic Visuomotor Architecture for Robotic Grasping. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_43
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