2,246 research outputs found
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
With widespread applications of artificial intelligence (AI), the
capabilities of the perception, understanding, decision-making and control for
autonomous systems have improved significantly in the past years. When
autonomous systems consider the performance of accuracy and transferability,
several AI methods, like adversarial learning, reinforcement learning (RL) and
meta-learning, show their powerful performance. Here, we review the
learning-based approaches in autonomous systems from the perspectives of
accuracy and transferability. Accuracy means that a well-trained model shows
good results during the testing phase, in which the testing set shares a same
task or a data distribution with the training set. Transferability means that
when a well-trained model is transferred to other testing domains, the accuracy
is still good. Firstly, we introduce some basic concepts of transfer learning
and then present some preliminaries of adversarial learning, RL and
meta-learning. Secondly, we focus on reviewing the accuracy or transferability
or both of them to show the advantages of adversarial learning, like generative
adversarial networks (GANs), in typical computer vision tasks in autonomous
systems, including image style transfer, image superresolution, image
deblurring/dehazing/rain removal, semantic segmentation, depth estimation,
pedestrian detection and person re-identification (re-ID). Then, we further
review the performance of RL and meta-learning from the aspects of accuracy or
transferability or both of them in autonomous systems, involving pedestrian
tracking, robot navigation and robotic manipulation. Finally, we discuss
several challenges and future topics for using adversarial learning, RL and
meta-learning in autonomous systems
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
A general-purpose intelligent robot must be able to learn autonomously and be
able to accomplish multiple tasks in order to be deployed in the real world.
However, standard reinforcement learning approaches learn separate
task-specific policies and assume the reward function for each task is known a
priori. We propose a framework that learns event cues from off-policy data, and
can flexibly combine these event cues at test time to accomplish different
tasks. These event cue labels are not assumed to be known a priori, but are
instead labeled using learned models, such as computer vision detectors, and
then `backed up' in time using an action-conditioned predictive model. We show
that a simulated robotic car and a real-world RC car can gather data and train
fully autonomously without any human-provided labels beyond those needed to
train the detectors, and then at test-time be able to accomplish a variety of
different tasks. Videos of the experiments and code can be found at
https://github.com/gkahn13/CAPsComment: Accepted to the Conference on Robot Learning (CoRL) 2018. Video at
https://youtu.be/lOLT7zifEk
Learning to Sequence Robot Behaviors for Visual Navigation
Recent literature in the robotics community has focused on learning robot
behaviors that abstract out lower-level details of robot control. To fully
leverage the efficacy of such behaviors, it is necessary to select and sequence
them to achieve a given task. In this paper, we present an approach to both
learn and sequence robot behaviors, applied to the problem of visual navigation
of mobile robots. We construct a layered representation of control policies
composed of low- level behaviors and a meta-level policy. The low-level
behaviors enable the robot to locomote in a particular environment while
avoiding obstacles, and the meta-level policy actively selects the low-level
behavior most appropriate for the current situation based purely on visual
feedback. We demonstrate the effectiveness of our method on three simulated
robot navigation tasks: a legged hexapod robot which must successfully traverse
varying terrain, a wheeled robot which must navigate a maze-like course while
avoiding obstacles, and finally a wheeled robot navigating in the presence of
dynamic obstacles. We show that by learning control policies in a layered
manner, we gain the ability to successfully traverse new compound environments
composed of distinct sub-environments, and outperform both the low-level
behaviors in their respective sub-environments, as well as a hand-crafted
selection of low-level policies on these compound environments
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent
years, with notable achievements such as Deepmind's AlphaGo. It has been
successfully deployed in commercial vehicles like Mobileye's path planning
system. However, a vast majority of work on DRL is focused on toy examples in
controlled synthetic car simulator environments such as TORCS and CARLA. In
general, DRL is still at its infancy in terms of usability in real-world
applications. Our goal in this paper is to encourage real-world deployment of
DRL in various autonomous driving (AD) applications. We first provide an
overview of the tasks in autonomous driving systems, reinforcement learning
algorithms and applications of DRL to AD systems. We then discuss the
challenges which must be addressed to enable further progress towards
real-world deployment.Comment: Accepted for Oral Presentation at VISAPP 201
Learning to Imagine Manipulation Goals for Robot Task Planning
Prospection is an important part of how humans come up with new task plans,
but has not been explored in depth in robotics. Predicting multiple task-level
is a challenging problem that involves capturing both task semantics and
continuous variability over the state of the world. Ideally, we would combine
the ability of machine learning to leverage big data for learning the semantics
of a task, while using techniques from task planning to reliably generalize to
new environment. In this work, we propose a method for learning a model
encoding just such a representation for task planning. We learn a neural net
that encodes the most likely outcomes from high level actions from a given
world. Our approach creates comprehensible task plans that allow us to predict
changes to the environment many time steps into the future. We demonstrate this
approach via application to a stacking task in a cluttered environment, where
the robot must select between different colored blocks while avoiding
obstacles, in order to perform a task. We also show results on a simple
navigation task. Our algorithm generates realistic image and pose predictions
at multiple points in a given task
A Survey of Deep Learning Techniques for Mobile Robot Applications
Advancements in deep learning over the years have attracted research into how
deep artificial neural networks can be used in robotic systems. This research
survey will present a summarization of the current research with a specific
focus on the gains and obstacles for deep learning to be applied to mobile
robotics
Deep Learning in Robotics: A Review of Recent Research
Advances in deep learning over the last decade have led to a flurry of
research in the application of deep artificial neural networks to robotic
systems, with at least thirty papers published on the subject between 2014 and
the present. This review discusses the applications, benefits, and limitations
of deep learning vis-\`a-vis physical robotic systems, using contemporary
research as exemplars. It is intended to communicate recent advances to the
wider robotics community and inspire additional interest in and application of
deep learning in robotics.Comment: 41 pages, 135 reference
Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks
Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017]
is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a
goal-directed control signal of an agent whose dynamics has previously been
learned during a training phase in an unsupervised manner. PFAx hardly requires
assumptions or prior knowledge of the agent's sensor or control mechanics, or
of the environment. It selects features from a high-dimensional input by
intrinsic predictability and organizes them into a reasonably low-dimensional
model.
While PFA obtains a well predictable model, PFAx yields a model ideally
suited for manipulations with predictable outcome. This allows for
goal-directed manipulation of an agent and thus for local navigation, i.e. for
reaching states where intermediate actions can be chosen by a permanent descent
of distance to the goal. The approach is limited when it comes to global
navigation, e.g. involving obstacles or multiple rooms.
In this article, we extend theoretical results from [Sprekeler and Wiskott,
2008], enabling PFAx to perform stable global navigation. So far, the most
widely exploited characteristic of Slow Feature Analysis (SFA) was that
slowness yields invariances. We focus on another fundamental characteristics of
slow signals: They tend to yield monotonicity and one significant property of
monotonicity is that local optimization is sufficient to find a global optimum.
We present an SFA-based algorithm that structures an environment such that
navigation tasks hierarchically decompose into subgoals. Each of these can be
efficiently achieved by PFAx, yielding an overall global solution of the task.
The algorithm needs to explore and process an environment only once and can
then perform all sorts of navigation tasks efficiently. We support this
algorithm by mathematical theory and apply it to different problems
A Brief Survey of Deep Reinforcement Learning
Deep reinforcement learning is poised to revolutionise the field of AI and
represents a step towards building autonomous systems with a higher level
understanding of the visual world. Currently, deep learning is enabling
reinforcement learning to scale to problems that were previously intractable,
such as learning to play video games directly from pixels. Deep reinforcement
learning algorithms are also applied to robotics, allowing control policies for
robots to be learned directly from camera inputs in the real world. In this
survey, we begin with an introduction to the general field of reinforcement
learning, then progress to the main streams of value-based and policy-based
methods. Our survey will cover central algorithms in deep reinforcement
learning, including the deep -network, trust region policy optimisation, and
asynchronous advantage actor-critic. In parallel, we highlight the unique
advantages of deep neural networks, focusing on visual understanding via
reinforcement learning. To conclude, we describe several current areas of
research within the field.Comment: IEEE Signal Processing Magazine, Special Issue on Deep Learning for
Image Understanding (arXiv extended version
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Intelligent robots need to achieve abstract objectives using concrete,
spatiotemporally complex sensory information and motor control. Tabula rasa
deep reinforcement learning (RL) has tackled demanding tasks in terms of either
visual, abstract, or physical reasoning, but solving these jointly remains a
formidable challenge. One recent, unsolved benchmark task that integrates these
challenges is Mujoban, where a robot needs to arrange 3D warehouses generated
from 2D Sokoban puzzles. We explore whether integrated tasks like Mujoban can
be solved by composing RL modules together in a sense-plan-act hierarchy, where
modules have well-defined roles similarly to classic robot architectures.
Unlike classic architectures that are typically model-based, we use only
model-free modules trained with RL or supervised learning. We find that our
modular RL approach dramatically outperforms the state-of-the-art monolithic RL
agent on Mujoban. Further, learned modules can be reused when, e.g., using a
different robot platform to solve the same task. Together our results give
strong evidence for the importance of research into modular RL designs. Project
website: https://sites.google.com/view/modular-rl
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