Intentional motion on-line learning and prediction

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “Predictive approaches based on dynamic properties of the objects are not able to provide correct long-term predictions of human motion [18] due to the fact that human motion also depends on other higher-level factors than kinematic constraints, such as plans or intentions.”
Planning-based prediction for pedestrians (2009, methodology): “Models based on mixtures of trajectories and conditioned action distribution modeling (using hidden Markov models) have been employed [17].”
A policy-blending formalism for shared control (2013, background): “Predicting intended motion, however, is usually again limited to classifying behaviors, or is done in low-dimensional spaces (Zollner et al., 2002; Vasquez et al., 2005; Ziebart et al., 2009).”
Human motion trajectory prediction: a survey (2019, methodology): “Unaware methods , which assume an open-space environment, e.g. (Foka and Trahanias 2010; Schneider and Gavrila 2013; Kruse and Wahl 1998; Bennewitz et al. 2002; Ellis et al. 2009; Jacobs et al. 2017; Vasquez et al. 2008; Unhelkar et al. 2015; Ferguson et al. 2015; Luber et al. 2012) 3.2.”