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A big challenge in the precision agriculture is the detection of fruits in coffee crops on agricultural environments. This paper presents a comparison of four features set to detect the red fruits (mature) in Coffee plants. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a coffee hall. The proposed methodology enables the extraction of visual features from image regions...
The process through which children learn about the world and develop perceptual, cognitive and motor skills relies heavily on object exploration in their physical world. New types of assistive technology that enable children with impairments to interact with their environment have emerged in recent years, and they could be beneficial for children's cognitive and perceptual skills development. Many...
Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation...
In daily life it is necessary to learn skills that can be applied in different tasks and different contexts. Usually these skills are acquired by observation or by direct physical training with another expert person. The critical point is to know which is the best possible way to achieve this knowledge acquisition. In this work we have proposed a collaborative environment where subjects with different...
Motor relearning after stroke is a lengthy process which should be continued after patients get discharged from the clinic. This project aims at developing a system for telerehabilitation which enables stroke patients to exercise at home autonomously or under supervision of a therapist. The system includes haptic therapy devices which are more promising and beneficial for stroke rehabilitation than...
Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to target-driven visual navigation. To...
3Sergey Levine is with Google Brain, Mountain View, CA 94043, USA. We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided policy search (GPS), which iteratively optimizes a set of local policies for specific...
Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational power, or insufficient memory for storing large maps of the entire environment. Thus, developing compact...
This study presents a portion of the results for the four-year Taiwan National Science Council Project, Development and Assessment of Science Courses Integrating Indigenous Culture and Collaborative Problem Solving(CPS). The research team has already conducted three years of teaching experiments at Atayal Rainbow Elementary School in Nan'ao Atayal tribes. Results demonstrate trained Atayal students...
Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires...
The development of reliable and robust visual recognition systems is a main challenge towards the deployment of autonomous robotic agents in unconstrained environments. Learning to recognize objects requires image representations that are discriminative to relevant information while being invariant to nuisances, such as scaling, rotations, light and background changes, and so forth. Deep Convolutional...
Visual localization is the process of finding the location of a camera from the appearance of the images it captures. In this work, we propose an observation model that allows the use of images for particle filter localization. To achieve this, we exploit the capabilities of Gaussian Processes to calculate the likelihood of the observation for any given pose, in contrast to methods which restrict...
We present in this paper a real-time method for visual categorization to do robot grasping. We describe an object database with SURF feature points which we quantify with the Kmeans clustering algorithm to make visual words. Then, we train a Support Vector Machine classifier having as entries the distribution of the bag of features extracted earlier. Likewise, we do object recognition using the SVM...
Virtual/mixed reality leveraging an encountered type haptic display will suffer difficulty if virtual and real objects are spatially discrepant. We propose a new method for resolving this issue, visual guidance. The visual guidance algorithm is defined and described in detail, and contrasted with a previously explored approach. The feasibility of the proposed algorithm is experimentally verified.
There has been significant research aimed at leveraging programmable robotic devices to provide haptic assistance or augmentation to a human user so that new motor skills can be trained efficiently and retained long after training has concluded. The success of these approaches has been varied, and retention of skill is typically not significantly better for groups exposed to these controllers during...
In open-ended domains, autonomous robots must have the ability to continuously process visual information, and execute learning and recognition in a concurrent and interleaved fashion. Because the set of categories to be learned is not predefined, it is not feasible to assume that one can pre-program all object categories required by service robots. Topic modelling approaches usually construct the...
Brain-machine interface (BMI) systems collect and classify electroencephalogram (EEG) data to predict the desired command of the user. The P300 EEG signal is passively produced when a user observes or hears a desired stimulus. The P300 can be used with a visual display to allow a BMI user to select commands from an array of selections. The visual stimuli are often repeated and averaged to increase...
Derived from ecological psychology, the term ‘affordance’ refers to the functional classification of objects. It simply means the set of actions a subject (i.e. humans and anthropomorphic agents) can possibly perform with an object. There are several paradigms in the researches regarding the approaches of affordance detection. These approaches include considering other contexts like the subject, ambient...
It is well known that image representations learned through ad-hoc dictionaries improve the overall results in object categorization problems. Following the widely accepted coding-pooling visual recognition pipeline, these representations are often tightly coupled with a coding stage. In this paper we show how to exploit ad-hoc representations both within the coding and the pooling phases. We learn...
The use of emotional states for Human-Robot Interaction (HRI) has attracted considerable attention in recent years. One of the most challenging tasks is to recognize the spontaneous expression of emotions, especially in an HRI scenario. Every person has a different way to express emotions, and this is aggravated by the complexity of interaction with different subjects, multimodal information and different...
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