572 research outputs found
Natural language generation for social robotics: Opportunities and challenges
In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit
socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face
social conversation is fluent, flexible linguistic interaction: as Bavelas et al. [1] point out, face-to-face dialogue is both the basic
form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful
non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker
and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted
verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes
for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the
field of computational linguistics devoted to the automated production of high-quality linguistic content: while this research area
is also an active one, in general most effort in NLG is focussed on producing high-quality written text. This article summarises
the state-of-the-art in the two individual research areas of social robotics and natural language generation. It then discusses
the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is
proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate
to the needs of socially interactive robots
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing
environment where robots learn from demonstration heavily relies on the correct
and safe operation of the robot. How this can be achieved is a challenge that
requires addressing both technical as well as human-centric research questions.
In this paper we discuss the state of the art in safety assurance, existing as
well as emerging standards in this area, and the need for new approaches to
safety assurance in the context of learning machines. We then focus on robotic
learning from demonstration, the challenges these techniques pose to safety
assurance and indicate opportunities to integrate safety considerations into
algorithms "by design". Finally, from a human-centric perspective, we stipulate
that, to achieve high levels of safety and ultimately trust, the robotic
co-worker must meet the innate expectations of the humans it works with. It is
our aim to stimulate a discussion focused on the safety aspects of
human-in-the-loop robotics, and to foster multidisciplinary collaboration to
address the research challenges identified
Amplifying Limitations, Harms and Risks of Large Language Models
We present this article as a small gesture in an attempt to counter what
appears to be exponentially growing hype around Artificial Intelligence (AI)
and its capabilities, and the distraction provided by the associated talk of
science-fiction scenarios that might arise if AI should become sentient and
super-intelligent. It may also help those outside of the field to become more
informed about some of the limitations of AI technology. In the current context
of popular discourse AI defaults to mean foundation and large language models
(LLMs) such as those used to create ChatGPT. This in itself is a
misrepresentation of the diversity, depth and volume of research, researchers,
and technology that truly represents the field of AI. AI being a field of
research that has existed in software artefacts since at least the 1950's. We
set out to highlight a number of limitations of LLMs, and in so doing highlight
that harms have already arisen and will continue to arise due to these
limitations. Along the way we also highlight some of the associated risks for
individuals and organisations in using this technology
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
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