55 research outputs found
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space
Combining model-based and model-free deep reinforcement learning has shown
great promise for improving sample efficiency on complex control tasks while
still retaining high performance. Incorporating imagination is a recent effort
in this direction inspired by human mental simulation of motor behavior. We
propose a learning-adaptive imagination approach which, unlike previous
approaches, takes into account the reliability of the learned dynamics model
used for imagining the future. Our approach learns an ensemble of disjoint
local dynamics models in latent space and derives an intrinsic reward based on
learning progress, motivating the controller to take actions leading to data
that improves the models. The learned models are used to generate imagined
experiences, augmenting the training set of real experiences. We evaluate our
approach on learning vision-based robotic grasping and show that it
significantly improves sample efficiency and achieves near-optimal performance
in a sparse reward environment.Comment: In: Proceedings of the Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo,
Norway, Aug. 19-22, 201
Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
Deep reinforcement learning has been shown to solve challenging tasks where
large amounts of training experience is available, usually obtained online
while learning the task. Robotics is a significant potential application domain
for many of these algorithms, but generating robot experience in the real world
is expensive, especially when each task requires a lengthy online training
procedure. Off-policy algorithms can in principle learn arbitrary tasks from a
diverse enough fixed dataset. In this work, we evaluate popular exploration
methods by generating robotics datasets for the purpose of learning to solve
tasks completely offline without any further interaction in the real world. We
present results on three popular continuous control tasks in simulation, as
well as continuous control of a high-dimensional real robot arm. Code
documenting all algorithms, experiments, and hyper-parameters is available at
https://github.com/qutrobotlearning/batchlearning
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
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