A natureza da ciência empírica segundo Berkeley

Tradução para o português do capítulo 5 do livro "Berkeley" (Oxford University Press, 1982), Cap. 5, p. 47-57. Republicado em The British Empiricists: Locke, Berkeley, Hume (Oxford University Press, 1992)

Motion Planning of Autonomous Vehicles on a Dual Carriageway without Speed Lanes

The problem of motion planning of an autonomous vehicle amidst other vehicles on a straight road is considered. Traffic in a number of countries is unorganized, where the vehicles do not move within predefined speed lanes. In this paper, we formulate a mechanism wherein an autonomous vehicle may travel on the “wrong” side in order to overtake a vehicle. Challenges include assessing a possible overtaking opportunity, cooperating with other vehicles, partial driving on the “wrong” side of the road and safely going to and returning from the “wrong” side. The experimental results presented show vehicles cooperating to accomplish overtaking manoeuvres

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

Educating through Exemplars: Alternative Paths to Virtue

This paper confronts Zagzebski’s exemplarism with the intertwined debates over the conditions of exemplarity and the unity-disunity of the virtues, to show the advantages of a pluralistic exemplar-based approach to moral education (PEBAME). PEBAME is based on a prima facie disunitarist perspective in moral theory, which amounts to admitting both exemplarity in all respects and single-virtue exemplarity. First, we account for the advantages of PEBAME, and we show how two figures in recent Italian history (Giorgio Perlasca and Gino Bartali) satisfy Blum’s definitions of ‘moral hero’ and ‘moral saint’ (1988). Then, we offer a comparative analysis of the effectiveness of heroes and saints with respect to character education, according to four criteria derived from PEBAME: admirability, virtuousness, transparency, and imitability. Finally, we conclude that both unitarist and disunitarist exemplars are fundamental to character education; this is because of the hero's superiority to the saint with respect to imitability, a fundamental feature of the exemplar for character education

Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

Saints, heroes, sages, and villains

This essay explores the question of how to be good. My starting point is a thesis about moral worth that I’ve defended in the past: roughly, that an action is morally worthy if and only it is performed for the reasons why it is right. While I think that account gets at one important sense of moral goodness, I argue here that it fails to capture several ways of being worthy of admiration on moral grounds. Moral goodness is more multi-faceted. My title is intended to capture that multi-facetedness: the essay examines saintliness, heroism, and sagacity. The variety of our common-sense moral ideals underscores the inadequacy of any one account of moral admirableness, and I hope to illuminate the distinct roles these ideals play in our everyday understanding of goodness. Along the way, I give an account of what makes actions heroic, of whether such actions are supererogatory, and of what, if anything, is wrong with moral deference. At the close of the essay, I begin to explore the flipside of these ideals: villainy

Sampling-based Algorithms for Optimal Motion Planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics Researc

Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective

We consider the problem of computing the maximum-reward motion in a reward field in an online setting. We assume that the robot has a limited perception range, and it discovers the reward field on the fly. We analyze the performance of a simple, practical lattice-based algorithm with respect to the perception range. Our main result is that, with very little perception range, the robot can collect as much reward as if it could see the whole reward field, under certain assumptions. Along the way, we establish novel connections between this class of problems and certain fundamental problems of nonequilibrium statistical mechanics . We demonstrate our results in simulation examples

Probabilistic lane estimation for autonomous driving using basis curves

Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made. This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways.United States. Defense Advanced Research Projects Agency (Urban Challenge, ARPA Order No. W369/00, Program Code DIRO, issued by DARPA/CMO under Contract No. HR0011-06-C-0149

A-B-C – Shaping Alphabets with Methods of Outdoor and Multisensory Learning

In this study the development of literacy skills is connected to outdoor learning, visual arts, and the thinking skills method. The study represents design research where the teachers and the researchers actively work together pursuing for more child-centered and motivating pedagogical approaches for learning literacy. The teaching experiment described in this article consists of playing with the shapes of alphabets, creating the forms of alphabets, and trying to find those alphabets in the outdoor environment, and empathizing with imaginary characters living in the immediate surroundings of the school. The methods used in the experiment brought child-centeredness and playfulness to learning. The holistic approach encouraged children to be active and supported their achieving a range of learning goals simultaneously and effectively