Collaborating Authors


New imaging method makes tiny robots visible in the body


How can a blood clot be removed from the brain without any major surgical intervention? How can a drug be delivered precisely into a diseased organ that is difficult to reach? Those are just two examples of the countless innovations envisioned by the researchers in the field of medical microrobotics. Tiny robots promise to fundamentally change future medical treatments: one day, they could move through patient's vasculature to eliminate malignancies, fight infections or provide precise diagnostic information entirely noninvasively. In principle, so the researchers argue, the circulatory system might serve as an ideal delivery route for the microrobots, since it reaches all organs and tissues in the body.

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 human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” 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 human–robot interactions.

Many Americans distrust emerging technology, new study finds


For more than a century, popular science fiction has promised us a future filled with robotics and AI technologies. In 2022, many of those dreams are being realized -- computers recognize us on sight and cars can drive themselves, we're building intelligent exoskeletons that multiply our strength and implanting computers in our skulls to augment our intelligence -- but that doesn't mean most of America trusts these breakthrough technologies any further than they can throw them. A recently published survey from Pew Research sought the opinions of some 10,260 US adults in November 2021 regarding their views on six technologies emerging in the fields of robotics and artificial intelligence/machine learning. Specifically, canvassers asked about both more mainstream systems like the use of facial recognition technology by police, the fake news-flagging algorithms used by social media platforms, and autonomous vehicle technology, as well as more cutting-edge ideas like brain-computer interfaces, gene editing and powered exoskeletons. The responses largely topped out at tepid, with minorities of respondents having even heard much about a given technology and even fewer willing to become early adopters once these systems are available to the general public.

Engineers develop a 'magnetic tentacle robot' for lung operations

Daily Mail - Science & tech

A bizarre'magnetic tentacle robot' that can pass into the narrow tubes of the lungs to take tissue samples could help save lives, a new study shows. Experts at the University of Leeds have created the device, which consists of external magnets and a'tentacle' – a thin polymer tube containing metallic particles. The so-called'tentacle' is highly flexible and measures just 0.07 of an inch (2 mm) in diameter, about twice the size of the tip of a ballpoint pen. Like something from a horror film, the tentacle would slowly enter the mouth or nose of a patient while they are under general anaesthetic. Guided by the external magnets, it could reach some of the smallest bronchial tubes in the lungs – and could be used to take tissue samples or deliver cancer therapy.

Motivating Physical Activity via Competitive Human-Robot Interaction Artificial Intelligence

Competition is ubiquitous in the natural world [1, 2] and in human society [3, 4, 5]. Despite its universality, competitive interaction has rarely been investigated in the field of Human Robot Interaction, which has mainly focused on cooperative interactions such as collaborative manipulation, mobility assistance, feeding, and so on [6, 7, 8, 9, 10]. In some ways it is not surprising that competitive interaction has been overlooked: of course everyone wants a robot that can assist them; who would want a robot that thwarts their intentions? Yet, we also accept that human-human competition can be healthy and productive, for example in structured contexts such as sports. In this paper we explore the idea that human-robot competition can provide similar benefits. We believe that physical exercise settings such as athletic practice, fitness training, and physical therapy are scenarios in which competitive HRI can benefit users.

Building Human-like Communicative Intelligence: A Grounded Perspective Artificial Intelligence

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, "grounded", perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building "grounded" linguistic intelligence. These components include embodying machines in a perception-action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world.

Sensory attenuation develops as a result of sensorimotor experience Artificial Intelligence

The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or is it acquired through learning? For decades, theoretical and biological studies have suggested related neural functions of sensory attenuation, such as an efference copy of the motor command and neuromodulation; however, the developmental aspect of sensory attenuation remains unexamined. Here, our simulation study using a recurrent neural network, operated according to a computational principle called free-energy minimization, shows that sensory attenuation can be developed as a free-energy state in the network through learning of two distinct types of sensorimotor patterns, characterized by self-produced or externally produced exteroceptive feedback. Simulation of the network, consisting of sensory (proprioceptive and exteroceptive), association, and executive areas, showed that shifts between these two types of sensorimotor patterns triggered transitions from one free-energy state to another in the network. Consequently, this induced shifts between attenuating and amplifying responses in the sensory areas. Furthermore, the executive area, proactively adjusted the precision of the prediction in lower levels while being modulated by the bottom-up sensory prediction error signal in minimizing the free-energy, thereby serving as an information hub in generating the observed shifts. We also found that innate alterations in modulation of sensory-information flow induced some characteristics analogous to schizophrenia and autism spectrum disorder. This study provides a novel perspective on neural mechanisms underlying emergence of perceptual phenomena and psychiatric disorders.

Randomized Classifiers vs Human Decision-Makers: Trustworthy AI May Have to Act Randomly and Society Seems to Accept This Artificial Intelligence

As \emph{artificial intelligence} (AI) systems are increasingly involved in decisions affecting our lives, ensuring that automated decision-making is fair and ethical has become a top priority. Intuitively, we feel that akin to human decisions, judgments of artificial agents should necessarily be grounded in some moral principles. Yet a decision-maker (whether human or artificial) can only make truly ethical (based on any ethical theory) and fair (according to any notion of fairness) decisions if full information on all the relevant factors on which the decision is based are available at the time of decision-making. This raises two problems: (1) In settings, where we rely on AI systems that are using classifiers obtained with supervised learning, some induction/generalization is present and some relevant attributes may not be present even during learning. (2) Modeling such decisions as games reveals that any -- however ethical -- pure strategy is inevitably susceptible to exploitation. Moreover, in many games, a Nash Equilibrium can only be obtained by using mixed strategies, i.e., to achieve mathematically optimal outcomes, decisions must be randomized. In this paper, we argue that in supervised learning settings, there exist random classifiers that perform at least as well as deterministic classifiers, and may hence be the optimal choice in many circumstances. We support our theoretical results with an empirical study indicating a positive societal attitude towards randomized artificial decision-makers, and discuss some policy and implementation issues related to the use of random classifiers that relate to and are relevant for current AI policy and standardization initiatives.

Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model Artificial Intelligence

These authors contributed equally to this work and should be considered co-first authors. Abstract: Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development. However, it is a challenge to detect flowering chrysanthemums under unstructured field environments given the variations on illumination, occlusion and object scale. In this context, we propose a highly fused and lightweight deep learning architecture based on YOLO for tea chrysanthemum detection (TC-YOLO). First, in the backbone component and neck component, the method uses the Cross-Stage Partially Dense Network (CSPDenseNet) as the main network, and embeds custom feature fusion modules to guide the gradient flow. In the final head component, the method combines the recursive feature pyramid (RFP) multiscale fusion reflow structure and the Atrous Spatial Pyramid Pool (ASPP) module with cavity convolution to achieve the detection task. The resulting model was tested on 300 field images, showing that under the NVIDIA Tesla P100 GPU environment, if the inference speed is 47.23 FPS for each image (416 416), TC-YOLO can achieve the average precision (AP) of 92.49% on our own tea chrysanthemum dataset. In addition, this method (13.6M) can be deployed on a single mobile GPU, and it could be further developed as a perception system for a selective chrysanthemum harvesting robot in the future. Keywords: Tea chrysanthemum; Flowering stage detection; Deep convolutional neural network; Agricultural robotics 1. Introduction Current studies show that tea chrysanthemums have significant commercial value (Liu et al., 2020; Liu et al., 2019). Not only that, but tea chrysanthemums can offer a range of health benefits (Hou et al., 2017; Yue et al., 2018). For example, they can significantly inhibit the activity of carcinogens and have distinct anti-aging, cholagogic and antihypertensive effects (Zheng et al., 2021).

Scientists develop a 3D-printed microneedle vaccine patch

Daily Mail - Science & tech

Scientists have developed a tiny 3D-printed microneedle vaccine patch that could offer a pain-free alternative to needles. In trials on mice, it offered a 10-fold greater immune response and a 50-fold greater T-cell and antigen-specific antibody response compared with a needle in the arm. The polymer patch, which is smaller than a 5p coin, needs lower doses and could be mailed to people's homes and self-administered, eliminating the need for trained medical personnel. It also offers an'anxiety-free' vaccination option for people who have a'needle phobia', also known as trypanophobia, which is putting some off getting their Covid jabs. The researchers are yet to conduct clinical trials of the patch on humans, which could pave the way for a new way of administering vaccines in the future.