2,486 research outputs found
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
We present a target-driven navigation system to improve mapless visual
navigation in indoor scenes. Our method takes a multi-view observation of a
robot and a target as inputs at each time step to provide a sequence of actions
that move the robot to the target without relying on odometry or GPS at
runtime. The system is learned by optimizing a combinational objective
encompassing three key designs. First, we propose that an agent conceives the
next observation before making an action decision. This is achieved by learning
a variational generative module from expert demonstrations. We then propose
predicting static collision in advance, as an auxiliary task to improve safety
during navigation. Moreover, to alleviate the training data imbalance problem
of termination action prediction, we also introduce a target checking module to
differentiate from augmenting navigation policy with a termination action. The
three proposed designs all contribute to the improved training data efficiency,
static collision avoidance, and navigation generalization performance,
resulting in a novel target-driven mapless navigation system. Through
experiments on a TurtleBot, we provide evidence that our model can be
integrated into a robotic system and navigate in the real world. Videos and
models can be found in the supplementary material.Comment: 11 pages, accepted by IEEE Robotics and Automation Letter
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