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Robotics

New submissions

[ total of 6 entries: 1-6 ]
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New submissions for Fri, 20 Apr 18

[1]  arXiv:1804.07028 [pdf, other]
Title: Line-based Road Structure Mapping Using Multi-beam LiDAR
Subjects: Robotics (cs.RO)

In this paper, we studied a line-based SLAM method for road structure mapping using multi-beam LiDAR. We propose to use the polyline as the basic mapping element instead of grid cell or point cloud, because the line-based representation is precise and lightweight, and it can directly generate vector-based HD map as demanded by autonomous driving systems. We explored: 1) The extraction and vectorization of road structures based on local probabilistic fusion. 2) The efficient line-based matching between frames of vectorized road structures. A specified road structure, the road boundary, is taken as an example. The results testified the feasibility and effectiveness of the proposed method. We applied our proposed mapping system in three different scenes and achieved the average absolute matching error of 0.07m, the average relative matching error of 8.64%.

[2]  arXiv:1804.07127 [pdf, other]
Title: Hierarchical Behavioral Repertoires with Unsupervised Descriptors
Comments: GECCO 2018
Journal-ref: Genetic and Evolutionary Computation Conference 2018
Subjects: Robotics (cs.RO); Neural and Evolutionary Computing (cs.NE)

Enabling artificial agents to automatically learn complex, versatile and high-performing behaviors is a long-lasting challenge. This paper presents a step in this direction with hierarchical behavioral repertoires that stack several behavioral repertoires to generate sophisticated behaviors. Each repertoire of this architecture uses the lower repertoires to create complex behaviors as sequences of simpler ones, while only the lowest repertoire directly controls the agent's movements. This paper also introduces a novel approach to automatically define behavioral descriptors thanks to an unsupervised neural network that organizes the produced high-level behaviors. The experiments show that the proposed architecture enables a robot to learn how to draw digits in an unsupervised manner after having learned to draw lines and arcs. Compared to traditional behavioral repertoires, the proposed architecture reduces the dimensionality of the optimization problems by orders of magnitude and provides behaviors with a twice better fitness. More importantly, it enables the transfer of knowledge between robots: a hierarchical repertoire evolved for a robotic arm to draw digits can be transferred to a humanoid robot by simply changing the lowest layer of the hierarchy. This enables the humanoid to draw digits although it has never been trained for this task.

[3]  arXiv:1804.07269 [pdf, other]
Title: Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills
Journal-ref: Autonomous Robots, Springer Verlag, 2014, 36 (3), pp.273-294
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Learning (cs.LG)

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.

[4]  arXiv:1804.07276 [pdf, other]
Title: Static and Dynamic Path Planning Using Incremental Heuristic Search
Authors: Asem Khattab
Comments: Internship Report
Subjects: Robotics (cs.RO)

Path planning is an important component in any highly automated vehicle system. In this report, the general problem of path planning is considered first in partially known static environments where only static obstacles are present but the layout of the environment is changing as the agent acquires new information. Attention is then given to the problem of path planning in dynamic environments where there are moving obstacles in addition to the static ones. Specifically, a 2D car-like agent traversing in a 2D environment was considered. It was found that the traditional configuration-time space approach is unsuitable for producing trajectories consistent with the dynamic constraints of a car. A novel scheme is then suggested where the state space is 4D consisting of position, speed and time but the search is done in the 3D space composed by position and speed. Simulation tests shows that the new scheme is capable of efficiently producing trajectories respecting the dynamic constraint of a car-like agent with a bound on their optimality.

Cross-lists for Fri, 20 Apr 18

[5]  arXiv:1804.07027 (cross-list from cs.CV) [pdf, other]
Title: VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View
Comments: 6 pages, 7 figures
Subjects: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

The automatic parking is being massively developed by car manufacturers and providers. The key to this system is the robust detection of parking slots and road structures, such as lane markings. In this paper, we proposed an HFCN-based segmentation method for parking slot and lane markings in a panoramic surround view (PSV) dataset. A surround view image is made of four calibrated images captured from four fisheye cameras. We collect and label more than 4,200 surround view images for this task, which contain various illuminated scenes of different types of parking slots. A VH-HFCN network is proposed, which adopts a highly fused convolutional network (HFCN) as the base, with an extra efficient VH-stage for better segmenting various markings. The VH-stage consists of two independent linear convolution paths with vertical and horizontal convolution kernels respectively. This modification enables the network to robustly and precisely extract linear features. We evaluate our model on the PSV dataset and the results show outstanding performance in ground markings segmentation. Based on the segmented markings, parking slots and lanes are acquired by skeletonization, hough line transform and line arrangement.

Replacements for Fri, 20 Apr 18

[6]  arXiv:1801.05297 (replaced) [pdf, other]
Title: Evidential Occupancy Grid Map Augmentation using Deep Learning
Comments: 7 pages, 5 figures
Subjects: Robotics (cs.RO)
[ total of 6 entries: 1-6 ]
[ showing up to 2000 entries per page: fewer | more ]

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