2,888 research outputs found
Autonomous Drifting Using Reinforcement Learning
Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
In this work, we present a rigorous end-to-end control strategy for
autonomous vehicles aimed at minimizing lap times in a time attack racing
event. We also introduce AutoRACE Simulator developed as a part of this
research project, which was employed to simulate accurate vehicular and
environmental dynamics along with realistic audio-visual effects. We adopted a
hybrid imitation-reinforcement learning architecture and crafted a novel reward
function to train a deep neural network policy to drive (using imitation
learning) and race (using reinforcement learning) a car autonomously in less
than 20 hours. Deployment results were reported as a direct comparison of 10
autonomous laps against 100 manual laps by 10 different human players. The
autonomous agent not only exhibited superior performance by gaining 0.96
seconds over the best manual lap, but it also dominated the human players by
1.46 seconds with regard to the mean lap time. This dominance could be
justified in terms of better trajectory optimization and lower reaction time of
the autonomous agent
Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control
Inertia drift is an aggressive transitional driving maneuver, which is
challenging due to the high nonlinearity of the system and the stringent
requirement on control and planning performance. This paper presents a solution
for the consecutive inertia drift of an autonomous RC car based on
primitive-based planning and data-driven control. The planner generates complex
paths via the concatenation of path segments called primitives, and the
controller eases the burden on feedback by interpolating between multiple real
trajectories with different initial conditions into one near-feasible reference
trajectory. The proposed strategy is capable of drifting through various paths
containing consecutive turns, which is validated in both simulation and
reality.Comment: 9 pages, 10 figures, to appear to IROS 202
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
We present a system that enables an autonomous small-scale RC car to drive
aggressively from visual observations using reinforcement learning (RL). Our
system, FastRLAP (faster lap), trains autonomously in the real world, without
human interventions, and without requiring any simulation or expert
demonstrations. Our system integrates a number of important components to make
this possible: we initialize the representations for the RL policy and value
function from a large prior dataset of other robots navigating in other
environments (at low speed), which provides a navigation-relevant
representation. From here, a sample-efficient online RL method uses a single
low-speed user-provided demonstration to determine the desired driving course,
extracts a set of navigational checkpoints, and autonomously practices driving
through these checkpoints, resetting automatically on collision or failure.
Perhaps surprisingly, we find that with appropriate initialization and choice
of algorithm, our system can learn to drive over a variety of racing courses
with less than 20 minutes of online training. The resulting policies exhibit
emergent aggressive driving skills, such as timing braking and acceleration
around turns and avoiding areas which impede the robot's motion, approaching
the performance of a human driver using a similar first-person interface over
the course of training
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV
This work explores the feasibility of steering a drone with a (recurrent)
neural network, based on input from a forward looking camera, in the context of
a high-level navigation task. We set up a generic framework for training a
network to perform navigation tasks based on imitation learning. It can be
applied to both aerial and land vehicles. As a proof of concept we apply it to
a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a
room containing a number of obstacles. So far only feedforward neural networks
(FNNs) have been used to train UAV control. To cope with more complex tasks, we
propose the use of recurrent neural networks (RNN) instead and successfully
train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision
based control is a sequential prediction problem, known for its highly
correlated input data. The correlation makes training a network hard,
especially an RNN. To overcome this issue, we investigate an alternative
sampling method during training, namely window-wise truncated backpropagation
through time (WW-TBPTT). Further, end-to-end training requires a lot of data
which often is not available. Therefore, we compare the performance of
retraining only the Fully Connected (FC) and LSTM control layers with networks
which are trained end-to-end. Performing the relatively simple task of crossing
a room already reveals important guidelines and good practices for training
neural control networks. Different visualizations help to explain the behavior
learned.Comment: 12 pages, 30 figure
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