85,166 research outputs found
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
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
We present a new method to translate videos to commands for robotic
manipulation using Deep Recurrent Neural Networks (RNN). Our framework first
extracts deep features from the input video frames with a deep Convolutional
Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are
then used to encode the visual features and sequentially generate the output
words as the command. We demonstrate that the translation accuracy can be
improved by allowing a smooth transaction between two RNN layers and using the
state-of-the-art feature extractor. The experimental results on our new
challenging dataset show that our approach outperforms recent methods by a fair
margin. Furthermore, we combine the proposed translation module with the vision
and planning system to let a robot perform various manipulation tasks. Finally,
we demonstrate the effectiveness of our framework on a full-size humanoid robot
WALK-MAN
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