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Learning Deep Features for Robotic Inference From Physical Interactions.

, , , , и . IEEE Trans. Cogn. Dev. Syst., 15 (3): 985-999 (сентября 2023)

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Learning Deep Features for Robotic Inference From Physical Interactions., , , , и . IEEE Trans. Cogn. Dev. Syst., 15 (3): 985-999 (сентября 2023)Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study., , , и . ICRA, стр. 8316-8322. IEEE, (2020)The Impact of Domain Randomization on Object Detection: A Case Study on Parametric Shapes and Synthetic Textures*., , , , , и . IROS, стр. 2593-2600. IEEE, (2019)Denoising auto-encoders for learning of objects and tools affordances in continuous space., , , и . ICRA, стр. 4866-4871. IEEE, (2016)Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study., , , и . CoRR, (2019)A robust and efficient framework for fast cylinder detection., , , , , и . Robotics Auton. Syst., (2019)SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics., , , и . IROS, стр. 9569-9576. IEEE, (2021)Shape-based attention for identification and localization of cylindrical objects., , , , и . ICDL-EPIROB, стр. 98-103. IEEE, (2017)Automatic generation of object shapes with desired functionalities., , и . CoRR, (2018)A generic visual perception domain randomisation framework for Gazebo., , , , , и . ICARSC, стр. 237-242. IEEE, (2018)