48,238 research outputs found
Combining goal inference and natural-language dialogue for human-robot joint action
We demonstrate how combining the reasoning components
from two existing systems designed for human-robot joint action
produces an integrated system with greater capabilities than either
of the individual systems. One of the systems supports primarily
non-verbal interaction and uses dynamic neural fields to infer the
user’s goals and to suggest appropriate system responses; the other
emphasises natural-language interaction and uses a dialogue manager
to process user input and select appropriate system responses.
Combining these two methods of reasoning results in a robot that is
able to coordinate its actions with those of the user while employing
a wide range of verbal and non-verbal communicative actions.(undefined
Teaching robot’s proactive behavior using human assistance
The final publication is available at link.springer.comIn recent years, there has been a growing interest in enabling autonomous social robots to interact with people. However, many questions remain unresolved regarding the social capabilities robots should have in order to perform this interaction in an ever more natural manner. In this paper, we tackle this problem through a comprehensive study of various topics involved in the interaction between a mobile robot and untrained human volunteers for a variety of tasks. In particular, this work presents a framework that enables the robot to proactively approach people and establish friendly interaction. To this end, we provided the robot with several perception and action skills, such as that of detecting people, planning an approach and communicating the intention to initiate a conversation while expressing an emotional status.We also introduce an interactive learning system that uses the person’s volunteered assistance to incrementally improve the robot’s perception skills. As a proof of concept, we focus on the particular task of online face learning and recognition. We conducted real-life experiments with our Tibi robot to validate the framework during the interaction process. Within this study, several surveys and user studies have been realized to reveal the social acceptability of the robot within the context of different tasks.Peer ReviewedPostprint (author's final draft
RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction
Robots have potential to revolutionize the way we interact with the world
around us. One of their largest potentials is in the domain of mobile health
where they can be used to facilitate clinical interventions. However, to
accomplish this, robots need to have access to our private data in order to
learn from these data and improve their interaction capabilities. Furthermore,
to enhance this learning process, the knowledge sharing among multiple robot
units is the natural step forward. However, to date, there is no
well-established framework which allows for such data sharing while preserving
the privacy of the users (e.g., the hospital patients). To this end, we
introduce RoboChain - the first learning framework for secure, decentralized
and computationally efficient data and model sharing among multiple robot units
installed at multiple sites (e.g., hospitals). RoboChain builds upon and
combines the latest advances in open data access and blockchain technologies,
as well as machine learning. We illustrate this framework using the example of
a clinical intervention conducted in a private network of hospitals.
Specifically, we lay down the system architecture that allows multiple robot
units, conducting the interventions at different hospitals, to perform
efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure
Exploring the role of trust and expectations in CRI using in-the-wild studies
Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child-Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies
Improving Robot Perception Skills Using a Fast Image-Labelling Method with Minimal Human Intervention
[EN] Featured Application Natural interface to enhance human-robot interactions. The aim is to improve robot perception skills. Robot perception skills contribute to natural interfaces that enhance human-robot interactions. This can be notably improved by using convolutional neural networks. To train a convolutional neural network, the labelling process is the crucial first stage, in which image objects are marked with rectangles or masks. There are many image-labelling tools, but all require human interaction to achieve good results. Manual image labelling with rectangles or masks is labor-intensive and unappealing work, which can take months to complete, making the labelling task tedious and lengthy. This paper proposes a fast method to create labelled images with minimal human intervention, which is tested with a robot perception task. Images of objects taken with specific backgrounds are quickly and accurately labelled with rectangles or masks. In a second step, detected objects can be synthesized with different backgrounds to improve the training capabilities of the image set. Experimental results show the effectiveness of this method with an example of human-robot interaction using hand fingers. This labelling method generates a database to train convolutional networks to detect hand fingers easily with minimal labelling work. This labelling method can be applied to new image sets or used to add new samples to existing labelled image sets of any application. This proposed method improves the labelling process noticeably and reduces the time required to start the training process of a convolutional neural network model.The Universitat Politecnica de Valencia has financed the open access fees of this paper with the project number 20200676 (Microinspeccion de superficies).Ricolfe Viala, C.; Blanes Campos, C. (2022). Improving Robot Perception Skills Using a Fast Image-Labelling Method with Minimal Human Intervention. Applied Sciences. 12(3):1-14. https://doi.org/10.3390/app1203155711412
NewsGPT: ChatGPT Integration for Robot-Reporter
The integration of large language models (LLMs) with social robots has
emerged as a promising avenue for enhancing human-robot interactions at a time
when news reports generated by artificial intelligence (AI) are gaining in
credibility. This integration is expected to intensify and become a more
productive resource for journalism, media, communication, and education. In
this paper a novel system is proposed that integrates AI's generative
pretrained transformer (GPT) model with the Pepper robot, with the aim of
improving the robot's natural language understanding and response generation
capabilities for enhanced social interactions. By leveraging GPT's powerful
language processing capabilities, this system offers a comprehensive pipeline
that incorporates voice input recording, speech-to-text transcription, context
analysis, and text-to-speech synthesis action generation. The Pepper robot is
enabled to comprehend user queries, generate informative responses with general
knowledge, maintain contextually relevant conversations, and act as a more
domain-oriented news reporter. It is also linked with a news resource and
powered with a Google search capability. To evaluate the performance of the
framework, experiments were conducted involving a set of diverse questions. The
robot's responses were assessed on the basis of eight criteria, including
relevance, context, and fluency. Despite some identified limitations, this
system contributes to the field of journalism and human-robot interaction by
showcasing the potential of integrating LLMs with social robots. The proposed
framework opens up opportunities for improving the conversational capabilities
of robots, enabling interactions that are smoother, more engaging, and more
context aware
Language-based sensing descriptors for robot object grounding
In this work, we consider an autonomous robot that is required
to understand commands given by a human through natural language.
Specifically, we assume that this robot is provided with an internal
representation of the environment. However, such a representation is unknown
to the user. In this context, we address the problem of allowing a
human to understand the robot internal representation through dialog.
To this end, we introduce the concept of sensing descriptors. Such representations
are used by the robot to recognize unknown object properties
in the given commands and warn the user about them. Additionally, we
show how these properties can be learned over time by leveraging past
interactions in order to enhance the grounding capabilities of the robot
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
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