2012/10/03 by Arturo Ribes, Jesús Cerquides, Ribes, Arturo +5
Computer Science · Neuroscience · #Advanced Vision and Imaging #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Machine Learning (cs.LG) #Robotics (cs.RO) #Visual perception and processing mechanisms #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.1210.1104
arxiv created 2012/10/03 · openalex publication_date 2012/10/03 · arxiv updated 2012/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In order to anticipate dangerous events, like a collision, an agent needs to make long-term predictions. However, those are challenging due to uncertainties in internal and external variables and environment dynamics. A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. We demonstrate that multi-modal predictions reduce to simpler distributions once actions are taken into account.