Jérôme Maye

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This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures. We(More)
This paper presents a novel approach to tracking dynamic objects in 3D range data. Its key contribution lies in the generative object detection algorithm which allows the tracker to robustly extract objects of varying sizes and shapes from the observations. In contrast to tracking methods using discriminative detectors, we are thus able to generalize over a(More)
We present a generic algorithm for self calibration of robotic systems that utilizes two key innovations. First, it uses information theoretic measures to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration(More)
The capability to overcome terrain irregularities or obstacles, named terrainability, is mostly dependant on the suspension mechanism of the rover and its control. For a given wheeled robot, the terrainability can be improved by using a sophisticated control, and is somewhat related to minimizing wheel slip. The proposed control method, named torque(More)
This paper presents a novel self-supervised on-line learning method to discover driving behaviors from data acquired with an inertial measurement unit (IMU) and a camera. Both sensors where mounted in a car that was driven by a human through a typical city environment with intersections, pedestrian crossings and traffic lights. The presented system extracts(More)
In this paper, we address the problem of curb detection for a pedestrian robot navigating in urban environments. We devise an unsupervised method that is mostly view-independent, makes no assumptions about the environment, restricts the set of hand-tuned parameters, and builds on sound probabilistic reasoning from the input data to the outcome of the(More)
We present a Bluetooth scatternet protocol (SNP) that provides the user with a serial link to all connected members in a transparent wireless Bluetooth (BT) network. By using only local decision making we can reduce the overhead of our scatternet protocol dramatically. We show how our SNP software layer simplifies a variety of tasks like the synchronization(More)
Every robotic system has some set of parameters—scale factors, sensor locations, link lengths, etc.—that are needed for state estimation, planning, and control. Despite best efforts during construction, some parameters will change over the lifetime of a robot due to normal wear and tear. In the best case, incorrect parameter values degrade performance. In(More)
Most large-scale public environments provide direction signs to facilitate the orientation for humans and to find their way to a goal location in the environment. Thus, for a robot operating in the same environment, it would be beneficial to interpret such signs correctly for a safe and efficient navigation. In this work, we propose a novel approach to(More)
The article presents the modular robot platform YaMoR, developed at the Biologically Inspired Robotics Group at EPFL, Switzerland. We give a short overview for the mechanical and electronic design of YaMoR. For wireless communication between modules we have developed a Scatternet Protocol (SNP) based on Bluetooth communication. We are interested in applying(More)