11,040 research outputs found
A distributed optimization framework for localization and formation control: applications to vision-based measurements
Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures
Online Ensemble Learning of Sensorimotor Contingencies
Forward models play a key role in cognitive agents by providing predictions of the sensory consequences of motor commands, also known as sensorimotor contingencies (SMCs). In continuously evolving environments, the ability to anticipate is fundamental in distinguishing cognitive from reactive agents, and it is particularly relevant for autonomous robots, that must be able to adapt their models in an online manner. Online learning skills, high accuracy of the forward models and multiple-step-ahead predictions are needed to enhance the robots’ anticipation capabilities. We propose an online heterogeneous ensemble learning method for building accurate forward models of SMCs relating motor commands to effects in robots’ sensorimotor system, in particular considering proprioception and vision. Our method achieves up to 98% higher accuracy both in short and long term predictions, compared to single predictors and other online and offline homogeneous ensembles. This method is validated on two different humanoid robots, namely the iCub and the Baxter
Learning to Singulate Objects using a Push Proposal Network
Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.deComment: International Symposium on Robotics Research (ISRR) 2017, videos:
http://robotpush.cs.uni-freiburg.d
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts
This paper presents a multi-robot system for manufacturing personalized
medical stent grafts. The proposed system adopts a modular design, which
includes: a (personalized) mandrel module, a bimanual sewing module, and a
vision module. The mandrel module incorporates the personalized geometry of
patients, while the bimanual sewing module adopts a learning-by-demonstration
approach to transfer human hand-sewing skills to the robots. The human
demonstrations were firstly observed by the vision module and then encoded
using a statistical model to generate the reference motion trajectories. During
autonomous robot sewing, the vision module plays the role of coordinating
multi-robot collaboration. Experiment results show that the robots can adapt to
generalized stent designs. The proposed system can also be used for other
manipulation tasks, especially for flexible production of customized products
and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial
Informatics, Key words: modularity, medical device customization, multi-robot
system, robot learning, visual servoing, robot sewin
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