14,782 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Emerging Linguistic Functions in Early Infancy
This paper presents results from experimental
studies on early language acquisition in infants and
attempts to interpret the experimental results within
the framework of the Ecological Theory of
Language Acquisition (ETLA) recently proposed
by (Lacerda et al., 2004a). From this perspective,
the infant’s first steps in the acquisition of the
ambient language are seen as a consequence of the
infant’s general capacity to represent sensory input
and the infant’s interaction with other actors in its
immediate ecological environment. On the basis of
available experimental evidence, it will be argued
that ETLA offers a productive alternative to
traditional descriptive views of the language
acquisition process by presenting an operative
model of how early linguistic function may emerge
through interaction
Egocentric Spatial Representation in Action and Perception
Neuropsychological findings used to motivate the “two visual systems” hypothesis have been taken to endanger a pair of widely accepted claims about spatial representation in visual experience. The first is the claim that visual experience represents 3-D space around the perceiver using an egocentric frame of reference. The second is the claim that there is a constitutive link between the spatial contents of visual experience and the perceiver’s bodily actions. In this paper, I carefully assess three main sources of evidence for the two visual systems hypothesis and argue that the best interpretation of the evidence is in fact consistent with both claims. I conclude with some brief remarks on the relation between visual consciousness and rational agency
Deep Visual Foresight for Planning Robot Motion
A key challenge in scaling up robot learning to many skills and environments
is removing the need for human supervision, so that robots can collect their
own data and improve their own performance without being limited by the cost of
requesting human feedback. Model-based reinforcement learning holds the promise
of enabling an agent to learn to predict the effects of its actions, which
could provide flexible predictive models for a wide range of tasks and
environments, without detailed human supervision. We develop a method for
combining deep action-conditioned video prediction models with model-predictive
control that uses entirely unlabeled training data. Our approach does not
require a calibrated camera, an instrumented training set-up, nor precise
sensing and actuation. Our results show that our method enables a real robot to
perform nonprehensile manipulation -- pushing objects -- and can handle novel
objects not seen during training.Comment: ICRA 2017. Supplementary video:
https://sites.google.com/site/robotforesight
- …