5,220 research outputs found
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
We present an approach for mobile robots to learn to navigate in dynamic
environments with pedestrians via raw depth inputs, in a socially compliant
manner. To achieve this, we adopt a generative adversarial imitation learning
(GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our
approach overcomes the disadvantages of previous methods, as they heavily
depend on the full knowledge of the location and velocity information of nearby
pedestrians, which not only requires specific sensors, but also the extraction
of such state information from raw sensory input could consume much computation
time. In this paper, our proposed GAIL-based model performs directly on raw
depth inputs and plans in real-time. Experiments show that our GAIL-based
approach greatly improves the safety and efficiency of the behavior of mobile
robots from pure behavior cloning. The real-world deployment also shows that
our method is capable of guiding autonomous vehicles to navigate in a socially
compliant manner directly through raw depth inputs. In addition, we release a
simulation plugin for modeling pedestrian behaviors based on the social force
model.Comment: ICRA 2018 camera-ready version. 7 pages, video link:
https://www.youtube.com/watch?v=0hw0GD3lkA
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
Robotic insertion tasks are characterized by contact and friction mechanics,
making them challenging for conventional feedback control methods due to
unmodeled physical effects. Reinforcement learning (RL) is a promising approach
for learning control policies in such settings. However, RL can be unsafe
during exploration and might require a large amount of real-world training
data, which is expensive to collect. In this paper, we study how to use
meta-reinforcement learning to solve the bulk of the problem in simulation by
solving a family of simulated industrial insertion tasks and then adapt
policies quickly in the real world. We demonstrate our approach by training an
agent to successfully perform challenging real-world insertion tasks using less
than 20 trials of real-world experience. Videos and other material are
available at https://pearl-insertion.github.io/Comment: 9 pages, 8 figure
Hierarchical Reinforcement Learning for Quadruped Locomotion
Legged locomotion is a challenging task for learning algorithms, especially
when the task requires a diverse set of primitive behaviors. To solve these
problems, we introduce a hierarchical framework to automatically decompose
complex locomotion tasks. A high-level policy issues commands in a latent space
and also selects for how long the low-level policy will execute the latent
command. Concurrently, the low-level policy uses the latent command and only
the robot's on-board sensors to control the robot's actuators. Our approach
allows the high-level policy to run at a lower frequency than the low-level
one. We test our framework on a path-following task for a dynamic quadruped
robot and we show that steering behaviors automatically emerge in the latent
command space as low-level skills are needed for this task. We then show
efficient adaptation of the trained policy to a different task by transfer of
the trained low-level policy. Finally, we validate the policies on a real
quadruped robot. To the best of our knowledge, this is the first application of
end-to-end hierarchical learning to a real robotic locomotion task
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
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
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling based path planning with reinforcement
learning (RL). The RL agents learn short-range, point-to-point navigation
policies that capture robot dynamics and task constraints without knowledge of
the large-scale topology. Next, the sampling-based planners provide roadmaps
which connect robot configurations that can be successfully navigated by the RL
agent. The same RL agents are used to control the robot under the direction of
the planning, enabling long-range navigation. We use the Probabilistic Roadmaps
(PRMs) for the sampling-based planner. The RL agents are constructed using
feature-based and deep neural net policies in continuous state and action
spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation
tasks with non-trivial robot dynamics: end-to-end differential drive indoor
navigation in office environments, and aerial cargo delivery in urban
environments with load displacement constraints. Our results show improvement
in task completion over both RL agents on their own and traditional
sampling-based planners. In the indoor navigation task, PRM-RL successfully
completes up to 215 m long trajectories under noisy sensor conditions, and the
aerial cargo delivery completes flights over 1000 m without violating the task
constraints in an environment 63 million times larger than used in training.Comment: 9 pages, 7 figure
Progress & Compress: A scalable framework for continual learning
We introduce a conceptually simple and scalable framework for continual
learning domains where tasks are learned sequentially. Our method is constant
in the number of parameters and is designed to preserve performance on
previously encountered tasks while accelerating learning progress on subsequent
problems. This is achieved by training a network with two components: A
knowledge base, capable of solving previously encountered problems, which is
connected to an active column that is employed to efficiently learn the current
task. After learning a new task, the active column is distilled into the
knowledge base, taking care to protect any previously acquired skills. This
cycle of active learning (progression) followed by consolidation (compression)
requires no architecture growth, no access to or storing of previous data or
tasks, and no task-specific parameters. We demonstrate the progress & compress
approach on sequential classification of handwritten alphabets as well as two
reinforcement learning domains: Atari games and 3D maze navigation.Comment: Accepted at ICML 201
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
Deep Reinforcement Learning has been successfully applied in various computer
games [8]. However, it is still rarely used in real-world applications,
especially for the navigation and continuous control of real mobile robots
[13]. Previous approaches lack safety and robustness and/or need a structured
environment. In this paper we present our proof of concept for autonomous
self-learning robot navigation in an unknown environment for a real robot
without a map or planner. The input for the robot is only the fused data from a
2D laser scanner and a RGB-D camera as well as the orientation to the goal. The
map of the environment is unknown. The output actions of an Asynchronous
Advantage Actor-Critic network (GA3C) are the linear and angular velocities for
the robot. The navigator/controller network is pretrained in a high-speed,
parallel, and self-implemented simulation environment to speed up the learning
process and then deployed to the real robot. To avoid overfitting, we train
relatively small networks, and we add random Gaussian noise to the input laser
data. The sensor data fusion with the RGB-D camera allows the robot to navigate
in real environments with real 3D obstacle avoidance and without the need to
fit the environment to the sensory capabilities of the robot. To further
increase the robustness, we train on environments of varying difficulties and
run 32 training instances simultaneously. Video: supplementary File / YouTube,
Code: GitHubComment: 7 pages, repor
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation
Reinforcement Learning (RL), among other learning-based methods, represents
powerful tools to solve complex robotic tasks (e.g., actuation, manipulation,
navigation, etc.), with the need for real-world data to train these systems as
one of its most important limitations. The use of simulators is one way to
address this issue, yet knowledge acquired in simulations does not work
directly in the real-world, which is known as the sim-to-real transfer problem.
While previous works focus on the nature of the images used as observations
(e.g., textures and lighting), which has proven useful for a sim-to-sim
transfer, they neglect other concerns regarding said observations, such as
precise geometrical meanings, failing at robot-to-robot, and thus in
sim-to-real transfers. We propose a method that learns on an observation space
constructed by point clouds and environment randomization, generalizing among
robots and simulators to achieve sim-to-real, while also addressing partial
observability. We demonstrate the benefits of our methodology on the point goal
navigation task, in which our method proves to be highly unaffected to unseen
scenarios produced by robot-to-robot transfer, outperforms image-based
baselines in robot-randomized experiments, and presents high performances in
sim-to-sim conditions. Finally, we perform several experiments to validate the
sim-to-real transfer to a physical domestic robot platform, confirming the
out-of-the-box performance of our system.Comment: Accepted to IROS'202
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
Autonomous navigation has become an increasingly popular machine learning
application. Recent advances in deep learning have also resulted in great
improvements to autonomous navigation. However, prior outdoor autonomous
navigation depends on various expensive sensors or large amounts of real
labeled data which is difficult to acquire and sometimes erroneous. The
objective of this study is to train an autonomous navigation model that uses a
simulator (instead of real labeled data) and an inexpensive monocular camera.
In order to exploit the simulator satisfactorily, our proposed method is based
on domain adaptation with adversarial learning. Specifically, we propose our
model with 1) a dilated residual block in the generator, 2) cycle loss, and 3)
style loss to improve the adversarial learning performance for satisfactory
domain adaptation. In addition, we perform a theoretical analysis that supports
the justification of our proposed method. We present empirical results of
navigation in outdoor courses with various intersections using a commercial
radio controlled car. We observe that our proposed method allows us to learn a
favorable navigation model by generating images with realistic textures. To the
best of our knowledge, this is the first work to apply domain adaptation with
adversarial learning to autonomous navigation in real outdoor environments. Our
proposed method can also be applied to precise image generation or other
robotic tasks
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