A Generative Model for Intention Recognition and Manipulation Assistance in Teleoperation
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Der Trend 'Alternde Gesellschaft' wirkt auf sämtliche Bereiche des täglichen Lebens und bedingt zum Beispiel eine zunehmende Lebensarbeitszeit, die gepaart mit stetig steigenden Anforderungen an Qualität und Effizienz neue Technologien, Arbeitsprozesse und ...
This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and control. A method is presented for translating CPPNs into complete robots includin ...
This paper presents an architecture for solving generically the problem of extracting the relevant features of a given task in a programming by demonstration framework and the problem of generalizing the acquired knowledge to various contexts. We validate ...
We present a series of experiments concerned with the inspection of regular, engineered structures carried out using swarms of five to twenty autonomous, miniature robots, solely endowed with onboard, local sensors. Individual robot controllers are behavio ...
This paper addresses qualitative and quantitative diversity and specialization issues in the framework of selforganizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous sw ...
We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a ti ...
A vital task in sports video annotation is to detect and segment areas of the playfield. This is an important first step in player or ball tracking and detecting the location of the play on the playfield. In this paper we present a technique using statisti ...
Robot Programming by Demonstration (PbD) aims at developing adaptive and robust controllers to enable the robot to learn new skills by observing and imitating a human demonstration. While the vast majority of PbD works focused on systems that learn a speci ...
This paper addresses qualitative and quantitative diversity and specialization issues in the frame- work of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous ...
Artificial evolution of computer software (evolutionary neural networks, genetic programming, evolutionary fuzzy systems, etc.) has been shown to generate software that in many cases is more performant than that designed by engineers. Evolved software perf ...