111 research outputs found
Pragmatic Frames for Teaching and Learning in Human-Robot interaction: Review and Challenges
Vollmer A-L, Wrede B, Rohlfing KJ, Oudeyer P-Y. Pragmatic Frames for Teaching and Learning in Human-Robot interaction: Review and Challenges. FRONTIERS IN NEUROROBOTICS. 2016;10: 10.One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning-teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human-human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; (2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching
Neuromorphic Computing for Interactive Robotics: A Systematic Review
Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics.We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots
Resonance as a design strategy for AI and social robots
Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within humanârobot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of âsympathetic resonanceâ as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in humanârobot interactions
Reinforcement Learning Approaches in Social Robotics
This article surveys reinforcement learning approaches in social robotics.
Reinforcement learning is a framework for decision-making problems in which an
agent interacts through trial-and-error with its environment to discover an
optimal behavior. Since interaction is a key component in both reinforcement
learning and social robotics, it can be a well-suited approach for real-world
interactions with physically embodied social robots. The scope of the paper is
focused particularly on studies that include social physical robots and
real-world human-robot interactions with users. We present a thorough analysis
of reinforcement learning approaches in social robotics. In addition to a
survey, we categorize existent reinforcement learning approaches based on the
used method and the design of the reward mechanisms. Moreover, since
communication capability is a prominent feature of social robots, we discuss
and group the papers based on the communication medium used for reward
formulation. Considering the importance of designing the reward function, we
also provide a categorization of the papers based on the nature of the reward.
This categorization includes three major themes: interactive reinforcement
learning, intrinsically motivated methods, and task performance-driven methods.
The benefits and challenges of reinforcement learning in social robotics,
evaluation methods of the papers regarding whether or not they use subjective
and algorithmic measures, a discussion in the view of real-world reinforcement
learning challenges and proposed solutions, the points that remain to be
explored, including the approaches that have thus far received less attention
is also given in the paper. Thus, this paper aims to become a starting point
for researchers interested in using and applying reinforcement learning methods
in this particular research field
Developmental Bootstrapping of AIs
Although some current AIs surpass human abilities in closed artificial worlds
such as board games, their abilities in the real world are limited. They make
strange mistakes and do not notice them. They cannot be instructed easily, fail
to use common sense, and lack curiosity. They do not make good collaborators.
Mainstream approaches for creating AIs are the traditional manually-constructed
symbolic AI approach and generative and deep learning AI approaches including
large language models (LLMs). These systems are not well suited for creating
robust and trustworthy AIs. Although it is outside of the mainstream, the
developmental bootstrapping approach has more potential. In developmental
bootstrapping, AIs develop competences like human children do. They start with
innate competences. They interact with the environment and learn from their
interactions. They incrementally extend their innate competences with
self-developed competences. They interact and learn from people and establish
perceptual, cognitive, and common grounding. They acquire the competences they
need through bootstrapping. However, developmental robotics has not yet
produced AIs with robust adult-level competences. Projects have typically
stopped at the Toddler Barrier corresponding to human infant development at
about two years of age, before their speech is fluent. They also do not bridge
the Reading Barrier, to skillfully and skeptically draw on the socially
developed information resources that power current LLMs. The next competences
in human cognitive development involve intrinsic motivation, imitation
learning, imagination, coordination, and communication. This position paper
lays out the logic, prospects, gaps, and challenges for extending the practice
of developmental bootstrapping to acquire further competences and create
robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure
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Memory and mental time travel in humans and social robots.
From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.Funding. The preparation of this chapter was supported by funding
from the EU Seventh Framework Programme as part of the projects
Experimental Functional Android Assistant (EFAA, FP7-ICT-270490)
and What You Say Is What You Did (WYSIWYD, FP7-ICT-612139)
and by the EU H2020 Programme as part of the Human Brain Project
(HBP-SGA1, 720270; HBP-SGA2, 785907).
Acknowledgements. The authors are grateful to Paul Verschure, Peter
Dominey, Giorgio Metta, Yiannis Demiris and the other members
of the WYSIWYD and EFAA consortia; to members of the HBP EPISENSE
group; and to our colleagues at the University of Sheffield
who have helped us to develop memory systems for the iCub, particularly
Luke Boorman, Harry Jackson and Matthew Evans. The
Sheffield iCub was purchased with the support of the UK Engineering
and Physical Sciences Research Council (EPSRC)
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