Nicolás Navarro-Guerrero

Leibniz Universität Hannover · L3S Research Center

Both nociception and punishment signals have been used in robotics. However, the potential for using these negatively valenced types of reinforcement learning signals for robot learning has not been exploited in detail yet. Nociceptive signals are primarily used as triggers of preprogrammed action sequences. Punishment signals are typically disembo...

Reinforcement learning algorithms and particularly those based on temporal-difference learning are widely adopted and have been successfully applied to a number of problems as well as used to model animal learning. However, they are based on neural pathways involved in reward-seeking behaviour since little is known about punishment-driven learning...

In computational systems for visuo-haptic object recognition, vision and haptics are often modeled as separate processes. But this is far from what really happens in the human brain, where cross- as well as multimodal interactions take place between the two sensory modalities. Generally, three main principles can be identified as underlying the pro...

Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional. We suggest using a lower-dimensional...

Reinforcement learning (RL) has become widely adopted in robot control. Despite many successes, one major persisting problem can be very low data efficiency. One solution is interactive feedback, which has been shown to speed up RL considerably. As a result, there is an abundance of different strategies, which are, however, primarily tested on disc...

Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today's robots. BioTac is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli. However, the sensor's non-linearity poses challenges in simulating its behavior. In this paper, we first investigate a BioTac...

Robot learning enables robots to acquire new knowledge and skills through experience and interaction with their environment. Robot learning involves developing algorithms that allow robots to learn autonomously, adapt to new situations, and improve their performance over time. Using machine learning, robots can analyze large amounts of data and ext...

Tactile sensors have been developed since the early '70s and have greatly improved, but there are still no widely adopted solutions. Various technologies, such as capacitive, piezoelectric, piezoresistive, optical, and magnetic, are used in haptic sensing. However, most sensors are not mechanically robust for many applications and cannot cope well...

The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologies for artificial vision and touch, the effective integration of these two sensory modalities in robotic applica...

The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologies for artificial vision and touch, the effective integration of these two sensory modalities in robotic applica...

Reinforcement learning (RL) has become widely adopted in robot control. Despite many successes, one major persisting problem can be very low data efficiency. One solution is interactive feedback, which has been shown to speed up RL considerably. As a result, there is an abundance of different strategies, which are, however, primarily tested on disc...

Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this...

This special issue includes state-of-the-art research on emerging topics on development and learning in natural and artificial systems. In addition to new submissions, the special issue includes extensions of the paper awarded the Best Paper Award at ICDL-EpiRob 2020—the 10th Joint IEEE Conference on Development and Learning and Epigenetic Roboti...

In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language processing (NLP) tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL...

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language processing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL method...

Multimodal object recognition is still an emerging field. Thus, publicly available datasets are still rare and of small size. This dataset was developed to help fill this void and presents multimodal data for 63 objects with some visual and haptic ambiguity. The dataset contains visual, kinesthetic and tactile (audio/vibrations) data. To completely...

The pursuit of higher levels of autonomy and versatility in robotics is arguably led by two main factors. Firstly, as we push robots out of the labs and productions lines, it becomes increasingly challenging to design for all possible scenarios that a particular robot might encounter. Secondly, the cost of designing, manufacturing, and maintaining...

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This study investigates the effects of using a mediation expert system under various support conditions in a laboratory setting. Information is provided to the system and advice retrieved either by the participants themselves, via a human mediator or via a tele-operated robot. We study the effects of these different ways of providing mediation supp...

Numerous studies expect negative effects of computerization by artificial intelligence and automation by robots on employment (Ford, 2015; Frey & Osborne, 2017). With advances in big data analytics and sensors even non‐routine cognitive and manual tasks might be assumed by computers and robots in the future (Frey & Osborne, 2017). Tasks that requir...

Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such perso...

A comparison between behavioural architectures, specifically a BDI architecture and a finite-state machine, for a collaborative package delivery system is presented. The system should assist a user in handling packages in cluttered environments. The entire system is built using open-source solutions for modules including speech recognition, person...

This paper describes the techniques used in the submitted video presenting an interaction scenario, realised using the Neuro-Inspired Companion (NICO) robot. NICO engages the users in a personalised conversation where the robot always tracks the users' face, remembers them and interacts with them using natural language. NICO can also learn to perfo...

Interdisciplinary research, drawing from robotics, artificial intelligence, neuroscience, psychology, and cognitive science, is a cornerstone to advance the state-of-the-art in multimodal human-robot interaction and neuro-cognitive mod-eling. Research on neuro-cognitive models benefits from the embodiment of these models into physical, humanoid age...

We present the robotic system IRMA (Interactive Robotic Memory Aid) that assists humans in their search for misplaced belongings within a natural home-like environment. Our stand-alone system integrates state-of-the-art approaches in a novel manner to achieve a seamless and intuitive human-robot interaction. IRMA directs its gaze toward the speaker...

The field of neurocognitive robotics takes the processing mechanisms of the brain as inspiration and guidance: computer implementations of robot perception and action should be based on brain-like neural architectures and biologically plausible learning mechanisms. Unsupervised learning and reinforcement learning have led to good results on the eme...

Giving interactive feedback, other than well done / badly done alone, can speed up reinforcement learning. However, the amount of feedback needed to improve the learning speed and performance has not been thoroughly investigated. To narrow this gap, we study the effects of one type of interaction: we allow the learner to ask a teacher whether the l...

Giving interactive feedback, other than well done / badly done alone, can speed up reinforcement learning. However, the amount of feedback needed to improve the learning speed and performance has not been thoroughly investigated. To narrow this gap, we study the effects of one type of interaction: we allow the learner to ask a teacher whether the l...

In this article we introduce a blackboard- based multiple agent system framework that considers biologically-based motivations as a means to develop a user friendly interface. The framework includes a population-based heuristic as well as a fuzzy logic-based inference system used toward scoring system behaviours. The heuristic provides an optimizat...

Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state s...

In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versatile robots, and on the other hand, because it can...

Abstract Classical fear conditioning has experienced a growing interest over the last decade. Fear learning mechanisms are a simple and robust learning paradigm that involves sensory and motor areas. We believe that a deeper study of these mechanisms will contribute not only to a better understanding of fear conditioning but also to the development...

In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of aut...

This paper describes the use of soft computing based techniques toward the acquisition of adaptive behaviors to be used in mobile exploration by cooperating robots. Navigation within unknown environments and the obtaining of dynamic behavior require some method of unsupervised learning given the impossibility of programming strategies to follow for...

In this paper we investigate real-time adaptive extensions of our fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. The main idea is to introduce active battery level sensors and recharge zones to improve robot behavior for reaching survivability in environment exploration. In...

This paper describes the use of soft computing techniques for acquiring adaptive behaviors to be used in mobile robot exploration. Action-based environment modeling (AEM) based navigation is used within unknown environments and unsupervised adaptive learning is used for obtaining of the dynamic behaviors. In this investigation it is shown that this...

In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior bas...