2019/01/15 by German Diez-Valencia, Takuya Ohashi, Diez-Valencia, German +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Psychology · #Action Observation and Synchronization #Animal Vocal Communication and Behavior #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multisensory perception and integration #Robotics (cs.RO) #cs.AI #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.1901.09792
Workshop on Crossmodal Learning for Intelligent Robotics. IEEE Int. Conference on Intelligent Robots and Systems (IROS 2018)
arxiv created 2019/01/15 · openalex publication_date 2019/01/15 · arxiv updated 2019/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Artificial self-perception is the machine ability to perceive its own body, i.e., the mastery of modal and intermodal contingencies of performing an action with a specific sensors/actuators body configuration. In other words, the spatio-temporal patterns that relate its sensors (e.g. visual, proprioceptive, tactile, etc.), its actions and its body latent variables are responsible of the distinction between its own body and the rest of the world. This paper describes some of the latest approaches for modelling artificial body self-perception: from Bayesian estimation to deep learning. Results show the potential of these free-model unsupervised or semi-supervised crossmodal/intermodal learning approaches. However, there are still challenges that should be overcome before we achieve artificial multisensory body perception.