15,291 research outputs found
Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot
Humans can experience fake body parts as theirs just by simple visuo-tactile
synchronous stimulation. This body-illusion is accompanied by a drift in the
perception of the real limb towards the fake limb, suggesting an update of body
estimation resulting from stimulation. This work compares body limb drifting
patterns of human participants, in a rubber hand illusion experiment, with the
end-effector estimation displacement of a multisensory robotic arm enabled with
predictive processing perception. Results show similar drifting patterns in
both human and robot experiments, and they also suggest that the perceptual
drift is due to prediction error fusion, rather than hypothesis selection. We
present body inference through prediction error minimization as one single
process that unites predictive coding and causal inference and that it is
responsible for the effects in perception when we are subjected to intermodal
sensory perturbations.Comment: Proceedings of the 2018 IEEE International Conference on Development
and Learning and Epigenetic Robotic
An adaptive appearance-based map for long-term topological localization of mobile robots
This work considers a mobile service robot which uses an appearance-based representation of its workplace as a map, where the current view and the map are used to estimate the current position in the environment. Due to the nature of real-world environments such as houses and offices, where the appearance keeps changing, the internal representation may become out of date after some time. To solve this problem the robot needs to be able to adapt its internal representation continually to the changes in the environment. This paper presents a method for creating an adaptive map for long-term appearance-based localization of a mobile robot using long-term and short-term memory concepts, with omni-directional vision as the external sensor
Deep Object-Centric Representations for Generalizable Robot Learning
Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning
Adaptive Deep Learning through Visual Domain Localization
A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision
Evaluation of Using Semi-Autonomy Features in Mobile Robotic Telepresence Systems
Mobile robotic telepresence systems used for social interaction scenarios require that users steer robots in a remote environment. As a consequence, a heavy workload can be put on users if they are unfamiliar with using robotic telepresence units. One way to lessen this workload is to automate certain operations performed during a telepresence session in order to assist remote drivers in navigating the robot in new environments. Such operations include autonomous robot localization and navigation to certain points in the home and automatic docking of the robot to the charging station. In this paper we describe the implementation of such autonomous features along with user evaluation study. The evaluation scenario is focused on the first experience on using the system by novice users. Importantly, that the scenario taken in this study assumed that participants have as little as possible prior information about the system. Four different use-cases were identified from the user behaviour analysis.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Plan Nacional de Investigación, proyecto DPI2011-25483
Gas Source Localization Strategies for Teleoperated Mobile Robots. An Experimental Analysis
Gas source localization (GSL) is one of the most important and direct applications of a gas sensitive mobile robot, and consists in searching for one or multiple volatile
emission sources with a mobile robot that has improved sensing
capabilities (i.e. olfaction, wind flow, etc.). This work adresses GSL by employing a teleoperated mobile robot, and focuses on
which search strategy is the most suitable for this teleoperated approach. Four different search strategies, namely chemotaxis,
anemotaxis, gas-mapping, and visual-aided search, are analyzed
and evaluated according to a set of proposed indicators (e.g. accuracy,
efficiency, success rate, etc.) to determine the most suitable
one for a human-teleoperated mobile robot. Experimental validation is carried out employing a large dataset composed of over 150 trials where volunteer operators had to locate a gas-leak in a virtual environment under various and realistic environmental conditions (i.e. different wind flow patterns and gas source locations). We report different findings, from which we highlight that, against intuition, visual-aided search is not always the best strategy, but depends on the environmental conditions and the operator’s ability to understand how gas distributes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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