3,626 research outputs found
Memory Augmented Control Networks
Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments
Evolving connection weights between sensors and actuators in robots
International Symposium on Industrial Electronics. Guimaraes, 7-11 July 1997.In this paper, an evolution strategy (ES) is introduced, to learn reactive behaviour in autonomous robots. An ES is used to learn high-performance reactive behaviour for navigation and collisions avoidance. The learned behaviour is able to solve the problem in a dynamic environment; so, the learning process has proven the ability to obtain generalised behaviours. The robot starts without information about the right associations between sensors and actuators, and, from this situation, the robot is able to learn, through experience, to reach the highest adaptability grade to the sensors information. No subjective information about âhow to accomplish the taskâ is included in the fitness function. A mini-robot Khepera has been used to test the learned behaviour
Decision tree learning for intelligent mobile robot navigation
The replication of human intelligence, learning and reasoning by means of computer
algorithms is termed Artificial Intelligence (Al) and the interaction of such
algorithms with the physical world can be achieved using robotics. The work described in
this thesis investigates the applications of concept learning (an approach which takes its
inspiration from biological motivations and from survival instincts in particular) to robot
control and path planning. The methodology of concept learning has been applied using
learning decision trees (DTs) which induce domain knowledge from a finite set of training
vectors which in turn describe systematically a physical entity and are used to train a robot
to learn new concepts and to adapt its behaviour.
To achieve behaviour learning, this work introduces the novel approach of hierarchical
learning and knowledge decomposition to the frame of the reactive robot architecture.
Following the analogy with survival instincts, the robot is first taught how to survive in
very simple and homogeneous environments, namely a world without any disturbances or
any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex
environments by adding further worlds to its existing knowledge. The repertoire of the
robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered
decision trees (DTs) accommodating a number of primitives. To classify robot perceptions,
control rules are synthesised using symbolic knowledge derived from searching the
hierarchy of DTs.
A second novel concept is introduced, namely that of multi-dimensional fuzzy associative
memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained
locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to
deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs.
In this thesis, the feasibility of the developed techniques is illustrated in the robot
applications, their benefits and drawbacks are discussed
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
Multiple Waypoint Navigation in Unknown Indoor Environments
Indoor motion planning focuses on solving the problem of navigating an agent
through a cluttered environment. To date, quite a lot of work has been done in
this field, but these methods often fail to find the optimal balance between
computationally inexpensive online path planning, and optimality of the path.
Along with this, these works often prove optimality for single-start
single-goal worlds. To address these challenges, we present a multiple waypoint
path planner and controller stack for navigation in unknown indoor environments
where waypoints include the goal along with the intermediary points that the
robot must traverse before reaching the goal. Our approach makes use of a
global planner (to find the next best waypoint at any instant), a local planner
(to plan the path to a specific waypoint), and an adaptive Model Predictive
Control strategy (for robust system control and faster maneuvers). We evaluate
our algorithm on a set of randomly generated obstacle maps, intermediate
waypoints, and start-goal pairs, with results indicating a significant
reduction in computational costs, with high accuracies and robust control.Comment: Accepted at ICCR 202
Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learning
Multi-robot navigation is a challenging task in which multiple robots must be
coordinated simultaneously within dynamic environments. We apply deep
reinforcement learning (DRL) to learn a decentralized end-to-end policy which
maps raw sensor data to the command velocities of the agent. In order to enable
the policy to generalize, the training is performed in different environments
and scenarios. The learned policy is tested and evaluated in common multi-robot
scenarios like switching a place, an intersection and a bottleneck situation.
This policy allows the agent to recover from dead ends and to navigate through
complex environments.Comment: 13 page
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