31 research outputs found
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
Obstacle avoidance is a fundamental requirement for autonomous robots which
operate in, and interact with, the real world. When perception is limited to
monocular vision avoiding collision becomes significantly more challenging due
to the lack of 3D information. Conventional path planners for obstacle
avoidance require tuning a number of parameters and do not have the ability to
directly benefit from large datasets and continuous use. In this paper, a
dueling architecture based deep double-Q network (D3QN) is proposed for
obstacle avoidance, using only monocular RGB vision. Based on the dueling and
double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a
simulator even with very noisy depth information predicted from RGB image.
Extensive experiments show that D3QN enables twofold acceleration on learning
compared with a normal deep Q network and the models trained solely in virtual
environments can be directly transferred to real robots, generalizing well to
various new environments with previously unseen dynamic objects.Comment: Accepted by RSS 2017 workshop New Frontiers for Deep Learning in
Robotic
Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has been applied successfully to many
robotic applications. However, the large number of trials needed for training
is a key issue. Most of existing techniques developed to improve training
efficiency (e.g. imitation) target on general tasks rather than being tailored
for robot applications, which have their specific context to benefit from. We
propose a novel framework, Assisted Reinforcement Learning, where a classical
controller (e.g. a PID controller) is used as an alternative, switchable policy
to speed up training of DRL for local planning and navigation problems. The
core idea is that the simple control law allows the robot to rapidly learn
sensible primitives, like driving in a straight line, instead of random
exploration. As the actor network becomes more advanced, it can then take over
to perform more complex actions, like obstacle avoidance. Eventually, the
simple controller can be discarded entirely. We show that not only does this
technique train faster, it also is less sensitive to the structure of the DRL
network and consistently outperforms a standard Deep Deterministic Policy
Gradient network. We demonstrate the results in both simulation and real-world
experiments.Comment: Published in ICRA2018. The code is now available at
https://github.com/xie9187/AsDDP
GraphTinker: Outlier Rejection and Inlier Injection for Pose Graph SLAM
In pose graph Simultaneous Localization and
Mapping (SLAM) systems, incorrect loop closures can seriously
hinder optimizers from converging to correct solutions,
significantly degrading both localization accuracy and map
consistency. Therefore, it is crucial to enhance their robustness
in the presence of numerous false-positive loop closures.
Existing approaches tend to fail when working with very
unreliable front-end systems, where the majority of inferred
loop closures are incorrect. In this paper, we propose a novel
middle layer, seamlessly embedded between front and back
ends, to boost the robustness of the whole SLAM system.
The main contributions of this paper are two-fold: 1) the
proposed middle layer offers a new mechanism to reliably
detect and remove false-positive loop closures, even if they
form the overwhelming majority; 2) artificial loop closures are
automatically reconstructed and injected into pose graphs in
the framework of an Extended Rauch-Tung-Striebel smoother,
reinforcing reliable loop closures. The proposed algorithm alters
the graph generated by the front-end and can then be optimized
by any back-end system. Extensive experiments are conducted
to demonstrate significantly improved accuracy and robustness
compared with state-of-the-art methods and various back-ends,
verifying the effectiveness of the proposed algorithm
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (DEFO-NET), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-netinherits the generalisation properties of GANs. This means that the network is able to reconstruct the whole 3-D appearance of the object given a single depth view of the object and to generalise to unseen object configurations. Contrary to traditional finite element methods, our approach is fast enough to be used in real-time applications. We apply the network to the problem of safe and fast navigation of mobile robots carrying payloads over different obstacles and floor materials. Experimental results in real scenarios show how a robot equipped with an RGB-D camera can use the network to predict terrain deformations under different payload configurations and use this to avoid unsafe areas
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Modelling the physical properties of everyday objects is a fundamental
prerequisite for autonomous robots. We present a novel generative adversarial
network (Defo-Net), able to predict body deformations under external forces
from a single RGB-D image. The network is based on an invertible conditional
Generative Adversarial Network (IcGAN) and is trained on a collection of
different objects of interest generated by a physical finite element model
simulator. Defo-Net inherits the generalisation properties of GANs. This means
that the network is able to reconstruct the whole 3-D appearance of the object
given a single depth view of the object and to generalise to unseen object
configurations. Contrary to traditional finite element methods, our approach is
fast enough to be used in real-time applications. We apply the network to the
problem of safe and fast navigation of mobile robots carrying payloads over
different obstacles and floor materials. Experimental results in real scenarios
show how a robot equipped with an RGB-D camera can use the network to predict
terrain deformations under different payload configurations and use this to
avoid unsafe areas.Comment: In ICRA 201