3,585 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
Deep Predictive Policy Training using Reinforcement Learning
Skilled robot task learning is best implemented by predictive action policies
due to the inherent latency of sensorimotor processes. However, training such
predictive policies is challenging as it involves finding a trajectory of motor
activations for the full duration of the action. We propose a data-efficient
deep predictive policy training (DPPT) framework with a deep neural network
policy architecture which maps an image observation to a sequence of motor
activations. The architecture consists of three sub-networks referred to as the
perception, policy and behavior super-layers. The perception and behavior
super-layers force an abstraction of visual and motor data trained with
synthetic and simulated training samples, respectively. The policy super-layer
is a small sub-network with fewer parameters that maps data in-between the
abstracted manifolds. It is trained for each task using methods for policy
search reinforcement learning. We demonstrate the suitability of the proposed
architecture and learning framework by training predictive policies for skilled
object grasping and ball throwing on a PR2 robot. The effectiveness of the
method is illustrated by the fact that these tasks are trained using only about
180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems 2017 (IROS2017
Inside the brain of an elite athlete: The neural processes that support high achievement in sports
Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance
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