8,734 research outputs found
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms
In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors
Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails
Robots hold promise in many scenarios involving outdoor use, such as
search-and-rescue, wildlife management, and collecting data to improve
environment, climate, and weather forecasting. However, autonomous navigation
of outdoor trails remains a challenging problem. Recent work has sought to
address this issue using deep learning. Although this approach has achieved
state-of-the-art results, the deep learning paradigm may be limited due to a
reliance on large amounts of annotated training data. Collecting and curating
training datasets may not be feasible or practical in many situations,
especially as trail conditions may change due to seasonal weather variations,
storms, and natural erosion. In this paper, we explore an approach to address
this issue through virtual-to-real-world transfer learning using a variety of
deep learning models trained to classify the direction of a trail in an image.
Our approach utilizes synthetic data gathered from virtual environments for
model training, bypassing the need to collect a large amount of real images of
the outdoors. We validate our approach in three main ways. First, we
demonstrate that our models achieve classification accuracies upwards of 95% on
our synthetic data set. Next, we utilize our classification models in the
control system of a simulated robot to demonstrate feasibility. Finally, we
evaluate our models on real-world trail data and demonstrate the potential of
virtual-to-real-world transfer learning.Comment: iROS 201
An evaluation of two distributed deployment algorithms for Mobile Wireless Sensor Networks
Deployment is important in large wireless sensor networks (WSN), specially because nodes may fall due to failure or battery issues. Mobile WSN cope with deployment and reconfiguration at the same time: nodes may move autonomously: i) to achieve a good area coverage; and ii) to distribute as homogeneously as possible. Optimal distribution is computationally expensive and implies high tra c load, so local, distributed approaches may be preferable. This paper presents an experimental evaluation of role-based and behavior based ones. Results show that the later
are better, specially for a large number of nodes in areas with obstacles.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
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