53,381 research outputs found
Uncertain Flow Visualization using LIC
In this paper we look at the Line Integral Convolution method for flow visualization and ways in which this can be
applied to the visualization of two dimensional, steady flow fields in the presence of uncertainty. To achieve this,
we start by studying the method and reviewing the history of modifications other authors have made to it in order
to improve its efficiency or capabilities, and using these as a base for the visualization of uncertain flow fields.
Finally, we apply our methodology to a case study from the field of oceanography
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
Children, Humanoid Robots and Caregivers
This paper presents developmental learning on a humanoid robot from human-robot interactions. We consider in particular teaching humanoids as children during the child's Separation and Individuation developmental phase (Mahler, 1979). Cognitive development during this phase is characterized both by the child's dependence on her mother for learning while becoming awareness of her own individuality, and by self-exploration of her physical surroundings. We propose a learning framework for a humanoid robot inspired on such cognitive development
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