53,102 research outputs found
Neural Networks in Mobile Robot Motion
This paper deals with a path planning and intelligent control of an
autonomous robot which should move safely in partially structured environment.
This environment may involve any number of obstacles of arbitrary shape and
size; some of them are allowed to move. We describe our approach to solving the
motion-planning problem in mobile robot control using neural networks-based
technique. Our method of the construction of a collision-free path for moving
robot among obstacles is based on two neural networks. The first neural network
is used to determine the "free" space using ultrasound range finder data. The
second neural network "finds" a safe direction for the next robot section of
the path in the workspace while avoiding the nearest obstacles. Simulation
examples of generated path with proposed techniques will be presented.Comment: 9 Page
Local Navigation Among Movable Obstacles with Deep Reinforcement Learning
Autonomous robots would benefit a lot by gaining the ability to manipulate
their environment to solve path planning tasks, known as the Navigation Among
Movable Obstacle (NAMO) problem. In this paper, we present a deep reinforcement
learning approach for solving NAMO locally, near narrow passages. We train
parallel agents in physics simulation using an Advantage Actor-Critic based
algorithm with a multi-modal neural network. We present an online policy that
is able to push obstacles in a non-axial-aligned fashion, react to unexpected
obstacle dynamics in real-time, and solve the local NAMO problem. Experimental
validation in simulation shows that the presented approach generalises to
unseen NAMO problems in unknown environments. We further demonstrate the
implementation of the policy on a real quadrupedal robot, showing that the
policy can deal with real-world sensor noises and uncertainties in unseen NAMO
tasks.Comment: 7 pages, 7 figures, 4 table
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Value Iteration Networks on Multiple Levels of Abstraction
Learning-based methods are promising to plan robot motion without performing
extensive search, which is needed by many non-learning approaches. Recently,
Value Iteration Networks (VINs) received much interest since---in contrast to
standard CNN-based architectures---they learn goal-directed behaviors which
generalize well to unseen domains. However, VINs are restricted to small and
low-dimensional domains, limiting their applicability to real-world planning
problems.
To address this issue, we propose to extend VINs to representations with
multiple levels of abstraction. While the vicinity of the robot is represented
in sufficient detail, the representation gets spatially coarser with increasing
distance from the robot. The information loss caused by the decreasing
resolution is compensated by increasing the number of features representing a
cell. We show that our approach is capable of solving significantly larger 2D
grid world planning tasks than the original VIN implementation. In contrast to
a multiresolution coarse-to-fine VIN implementation which does not employ
additional descriptive features, our approach is capable of solving challenging
environments, which demonstrates that the proposed method learns to encode
useful information in the additional features. As an application for solving
real-world planning tasks, we successfully employ our method to plan
omnidirectional driving for a search-and-rescue robot in cluttered terrain
End-to-End Navigation in Unknown Environments using Neural Networks
We investigate how a neural network can learn perception actions loops for
navigation in unknown environments. Specifically, we consider how to learn to
navigate in environments populated with cul-de-sacs that represent convex local
minima that the robot could fall into instead of finding a set of feasible
actions that take it to the goal. Traditional methods rely on maintaining a
global map to solve the problem of over coming a long cul-de-sac. However, due
to errors induced from local and global drift, it is highly challenging to
maintain such a map for long periods of time. One way to mitigate this problem
is by using learning techniques that do not rely on hand engineered map
representations and instead output appropriate control policies directly from
their sensory input. We first demonstrate that such a problem cannot be solved
directly by deep reinforcement learning due to the sparse reward structure of
the environment. Further, we demonstrate that deep supervised learning also
cannot be used directly to solve this problem. We then investigate network
models that offer a combination of reinforcement learning and supervised
learning and highlight the significance of adding fully differentiable memory
units to such networks. We evaluate our networks on their ability to generalize
to new environments and show that adding memory to such networks offers huge
jumps in performanceComment: Workshop on Learning Perception and Control for Autonomous Flight:
Safety, Memory and Efficiency, Robotics Science and Systems 201
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