1,093 research outputs found
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN
Collaborative robots are becoming more common on factory floors as well as
regular environments, however, their safety still is not a fully solved issue.
Collision detection does not always perform as expected and collision avoidance
is still an active research area. Collision avoidance works well for fixed
robot-camera setups, however, if they are shifted around, Eye-to-Hand
calibration becomes invalid making it difficult to accurately run many of the
existing collision avoidance algorithms. We approach the problem by presenting
a stand-alone system capable of detecting the robot and estimating its
position, including individual joints, by using a simple 2D colour image as an
input, where no Eye-to-Hand calibration is needed. As an extension of previous
work, a two-stage transfer learning approach is used to re-train a
multi-objective convolutional neural network (CNN) to allow it to be used with
heterogeneous robot arms. Our method is capable of detecting the robot in
real-time and new robot types can be added by having significantly smaller
training datasets compared to the requirements of a fully trained network. We
present data collection approach, the structure of the multi-objective CNN, the
two-stage transfer learning training and test results by using real robots from
Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible
application areas of our method together with the possible improvements.Comment: 6+n pages, ICRA 2019 submissio
Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images
The field of collaborative robotics and human-robot interaction often focuses
on the prediction of human behaviour, while assuming the information about the
robot setup and configuration being known. This is often the case with fixed
setups, which have all the sensors fixed and calibrated in relation to the rest
of the system. However, it becomes a limiting factor when the system needs to
be reconfigured or moved. We present a deep learning approach, which aims to
solve this issue. Our method learns to identify and precisely localise the
robot in 2D camera images, so having a fixed setup is no longer a requirement
and a camera can be moved. In addition, our approach identifies the robot type
and estimates the 3D position of the robot base in the camera image as well as
3D positions of each of the robot joints. Learning is done by using a
multi-objective convolutional neural network with four previously mentioned
objectives simultaneously using a combined loss function. The multi-objective
approach makes the system more flexible and efficient by reusing some of the
same features and diversifying for each objective in lower layers. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and an estimate of its base and joint positions in 3D. We
compare the results to our previous approach of using cascaded convolutional
neural networks.Comment: Ubiquitous Robots 2018 Regular paper submissio
Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning
In this paper, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. We train a deep neural network for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. The second simpler multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep network. In the experiments, we first compare different architectures to select the best deep network for location estimation, and then compare the effects of the multi-goal reinforcement learning method to traditional reinforcement learning. The results show a significant improvement when multi-goal reinforcement learning is used. Furthermore, the results of the location estimator show that a deep network can learn and generalize in different environments using camera images with high accuracy in both position and orientation
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