Learning Kinematic Models for Articulated Objects.
ABSTRACT Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.
Full-textDOI: · Available from: Christian Plagemann, Jul 04, 2015
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Conference Paper: Kinematic Evaluation of Articulated Rigid Objects[Show abstract] [Hide abstract]
ABSTRACT: This research is focused on providing solutions for kinematic evaluation of articulated rigid objects when noisy data is available. Recent results proposed different techniques which necessitate learning stages or large relative motions. Our approach starts from a complete analytical solution which uses only point and line features to parametrize the rigid motion. This analytical solution is built using orthogonal dual tensors generated by rigid basis of dual vectors. Next, the necessary transformations are added to the theoretical solution so it can be used with noisy data input. Thus, the computational procedure is revealed and different experiments are presented in order to underline the advantages of the proposed approach.18th International Conference on System Theory, Control and Computing; 10/2014
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ABSTRACT: The community-based generation of content has been tremendously successful in the World Wide Web - people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. RoboEarth is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform.Proceedings - IEEE International Conference on Robotics and Automation 01/2012; DOI:10.1109/ICRA.2012.6224812
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ABSTRACT: In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i.e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51.9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.Proceedings - IEEE International Conference on Robotics and Automation 01/2012; DOI:10.1109/ICRA.2012.6224929