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The paper describes human-interactive robot that supports gait training base on autonomous evaluation and navigation of human body movements. Robotic intervention in gait training is a promising method for prospective rehabilitation. In literature, gait training platforms such as power assisting limbs and body supporting mobile platforms have been studied well. These types of platforms, however, mainly...
This article presents the objectives of the project, Training of Trainers in Robotics for Schools in Vulnerable Areas of Costa Rica as well as the main activities, and the results that have been obtained in its first phase of execution. This is a joint project of the School of Informatics of the National University of Costa Rica (UNA), the Costa Rican Institute on Drugs (ICD) and the Ministry of Public...
In this work, we propose a framework to deal with cross-modal visuo-tactile object recognition. By cross-modal visuo-tactile object recognition, we mean that the object recognition algorithm is trained only with visual data and is able to recognize objects leveraging only tactile perception. The proposed cross-modal framework is constituted by three main elements. The first is a unified representation...
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world...
Recently, end-to-end learning frameworks are gaining prevalence in the field of robot control. These frameworks input states/images and directly predict the torques or the action parameters. However, these approaches are often critiqued due to their huge data requirements for learning a task. The argument of the difficulty in scalability to multiple tasks is well founded, since training these tasks...
Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy...
Artifacts such as voluntarily and involuntarily muscle movements are usually seen as a source of noise in EEG signals. In this paper, we see artifacts as a source of information in a signal. For example, eye movements can generate a traceable change in the EEG signals. We use eye movements as an effective marker for direction of movement. We propose two experiments for classification of four eye movement...
Human communicative behavior is both dynamic and bidirectional. This study aims to analyze such behavior by conducting imitative interactions between human subjects and a humanoid robot that has a neuro-dynamical system. For this purpose, we take a robot-centered approach in which the change in robot performance according to difference in human partner is analyzed, rather than adopting the typical...
In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different...
Engaging and motivational toys used during therapies for children with Autism Spectrum Disorder (ASD) need to be combined with the appropriated evaluation methods. Technology can provide the therapists with tools to facilitate the analysis of the interventions. In this paper we propose a model using paired devices with three different interaction rules made to facilitate turn-taking behaviors. These...
Hemiplegia is the lingering effect after a stroke and causes varying impairments. The ability to generate and modulate force is frequently impaired. This study focuses on a rehabilitation technique that encourages force generation in an arbitrary direction. The authors introduced a physiotherapy technique into a rehabilitation robot. Force visualization was provided as a feedback strategy to facilitate...
A humanoid robot capable of playing soccer needs to know where opponents and team mates are in the soccer field. The robot has to be able to recognize team mates and opponents, inferring information such as distance and estimated location of the other robots. In order to achieve this key requisite, this paper analyze two descriptor algorithms, HAAR and HOG, so that one of them can be used for recognizing...
After unilateral stroke, physical impairment commonly appears on one side of the body. As the unaffected side is the non-dominant side for some patients, we investigated whether human motor learning can transfer from a non-dominant arm to the dominant, with a 2-dof manipulandum that allows both isometric and dynamic reaching. The goal of our study was to assess the transfer of visuomotor adaptation...
Searching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to learn such an object-based visual saliency in an intrinsically motivated way using an environment exploration mechanism. We first define saliency in a geometrical manner and use this definition to discover salient elements given an attentive but costly...
Interactive technologies can help people acquire movement skills, and one way is by using visual distortions to boost neural adaptation. An extreme version of such approach is to train a movement without moving by creating a synesthetic illusion of movement - displaying virtual motions when there is none. While this approach uses no proprioceptive error to drive adaptation, our results show encouraging...
Dexterous hand and arm function are crucial for execution of activities of daily living (ADLs). However, it remains difficult for patients with neurological deficits, such as stroke, to recover the lost function. In this paper, a versatile haptic interface, omega. 7, has been employed to interact with the patient to perform designed dexterous manipulation tasks. Following the neurocognitive approach,...
Our aim is to better understand the action selection process of intelligent systems by looking at their ability of internal prediction. In robotic systems, one problem is to generate meaningful robot behaviour with a very small and simple set of trained motions. An additional problem is to compensate for incomplete sensory data while generating behaviour. We propose a new predictive action selector...
In the context of developmental robotics, a robot has to cope with complex sensorimotor spaces by reducing their dimensionality. In the case of sensor space reduction, classical approaches for pattern recognition use either hardcoded feature detection or supervised learning. We believe supervised learning and hard-coded feature extraction must be extended with unsupervised learning of feature representations...
Predicting the sensory consequences of an agent's own actions is considered an important skill for intelligent behavior. In terms of vision, so-called visual forward models can be applied to learn such predictions. This is no trivial task given the high-dimensionality of sensory data and complex action spaces. In this work, we propose to learn the visual consequences of changes in pan and tilt of...
Users of a brain-computer interface (BCI) learn to co-adapt with the system through the feedback they receive. Particularly in case of motor imagery BCIs, feedback design can play an important role in the course of motor imagery training. In this paper we investigated the effect of biased visual feedback on performance and motor imagery skills of users during BCI control of a pair of humanlike robotic...
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