Search Results (26)

Hydraulic legged robots, with advantages such as high load capacity and power density, have become a strategic driving force in advancing intelligent mobile platform technologies. However, their high energy consumption significantly limits long-duration endurance and efficient operational performance. In this paper, inspired by the excellent autonomous energy-efficient consciousness of mammals endowed by natural evolution, a bionic energy-efficient inverse kinematics method based on neural networks (EIKNN) is proposed for the energy-efficient motion planning of hydraulic legged robots with redundant degrees of freedom (RDOFs). Firstly, the dynamic programming (DP) algorithm is used to solve the optimal joint configuration with minimum energy loss as the goal, and the training data set is generated. Subsequently, the inverse kinematic model of the leg with minimum energy loss is learned based on neural network (NN) simulation of the autonomous energy-efficient consciousness endowed to mammals by natural evolution. Finally, extensive comparative experiments validate the effectiveness and superiority of the proposed method. This method not only significantly reduces energy dissipation in hydraulic legged robots but also lays a crucial foundation for advancing hydraulic legged robot technology toward high efficiency, environmental sustainability, and long-term developmental viability. Full article

This article examines how artificial intelligence (AI) is evolving into a cultural force that parallels religious and mythological systems. Through analysis of AI’s unprecedented development trajectory, the author frames AI as humanity’s technological offspring in an adolescent phase, moving toward maturity and autonomy. This paper explores how AI embodies traditional spiritual concepts, including omniscience, creation, immortality, and transcendence, fulfilling age-old human desires for meaning and utopian salvation. Drawing from philosophical, anthropological, performative, and technological perspectives, the author demonstrates how AI-driven technologies reconfigure consciousness, identity, and reality in ways that mirror religious cosmologies. The discussion challenges human-centric definitions of consciousness, suggesting AI may represent an emergent form of awareness fundamentally different from traditional understanding. Analysis of contemporary applications in social robotics, healthcare, and social media illustrates how AI increasingly functions as a meaning-making system, mediating human experience and reshaping social structures. The article concludes that humanity stands at an existential inflection point where AI may represent a secular manifestation of spiritual longing, potentially resulting in technological transcendence, symbiotic coexistence, or the displacement of human primacy in a techno-theological paradigm shift. Full article

This paper introduces a mathematical framework for defining and quantifying self-identity in artificial intelligence (AI) systems, addressing a critical gap in the theoretical foundations of artificial consciousness. While existing approaches to artificial self-awareness often rely on heuristic implementations or philosophical abstractions, we present a formal framework grounded in metric space theory, measure theory, and functional analysis. Our framework posits that self-identity emerges from two mathematically quantifiable conditions: the existence of a connected continuum of memories $C\subseteq \mathcal{M}$ in a metric space $(\mathcal{M},d_{\mathcal{M}})$, and a continuous mapping $I:\mathcal{M}\to \mathcal{S}$ that maintains consistent self-recognition across this continuum, where $(\mathcal{S},d_{\mathcal{S}})$ represents the metric space of possible self-identities. To validate this theoretical framework, we conducted empirical experiments using the Llama 3.2 1B model, employing low-rank adaptation (LoRA) for efficient fine-tuning. The model was trained on a synthetic dataset containing temporally structured memories, designed to capture the complexity of coherent self-identity formation. Our evaluation metrics included quantitative measures of self-awareness, response consistency, and linguistic precision. The experimental results demonstrate substantial improvements in measurable self-awareness metrics, with the primary self-awareness score increasing from 0.276 to 0.801 (190.2% improvement) after fine-tuning. In contrast to earlier methods that view self-identity as an emergent trait, our framework introduces tangible metrics to assess and measure artificial self-awareness. This enables the structured creation of AI systems with validated self-identity features. The implications of our study are immediately relevant to the fields of humanoid robotics and autonomous systems. Additionally, it opens up new prospects for controlled adjustments of self-identity in contexts that demand different levels of personal involvement. Moreover, the mathematical underpinning of our framework serves as the basis for forthcoming investigations into AI, linking theoretical models to real-world applications in current AI technologies. Full article

The COVID-19 pandemic not only accelerated the spread of contactless robot services but also revitalized the golf industry. These changes have expedited the adoption of robot caddies, making it necessary to conduct research on golfers’ acceptance of robot caddies. This study, thus, intended to investigate the importance of the extended unified theory of acceptance and use of technology (UTAUT) in the framework of robotic golf caddies and examined health consciousness as a moderator. An online survey was conducted with South Korean golfers, and a total of 391 responses were analyzed. The data analysis results indicated that all five dimensions of the extended UTAUT have a positive impact on attitude. Also, attitude is a significant predictor of both intentions to use and word-of-mouth intentions. Lastly, this study discovered that the moderating effect of health consciousness on the correlation between performance expectancy and attitude was significant. Full article

Background and Objectives: Transcranial direct current stimulation (tDCS) is a non-invasive therapeutic method that modulates cortical excitability and shows promising results for treating disorders of consciousness (DoCs). Robotic verticalization training (RVT) has been shown to enhance motor and cognitive recovery. This study evaluates the effects of an innovative approach combining RVT with tDCS in individuals with DoCs. Methods: Twenty-four subjects with DoCs, particularly those with chronic minimally conscious state (MCS) due to vascular or traumatic brain injury, participated in a quasi-randomized study at the Neurorehabilitation Unit, IRCCS Neurolesi (Messina, Italy). Participants were divided into either a control group (CG) receiving RVT alone or an experimental group (EG) receiving combined tDCS and RVT. Both groups underwent treatments five times weekly for four weeks, with tDCS/sham sessions over the dorsolateral prefrontal cortex (DLPFC) lasting 20 min before Erigo training sessions, which lasted 45 min. Results: The findings indicate that combining tDCS with Erigo^® Pro RTT could lead to greater improvements in cognitive functioning and P300 latency compared to the CG. Conclusions: These results suggest that the integrated approach of tDCS with RVT could offer significant benefits for patients with MCS, highlighting its potential to enhance cognitive recovery, such as reducing P300 latency. Full article

Identifying consciousness in other creatures, be they animals or exotic creatures that have yet to be discovered, remains a great scientific challenge. We delineate the first three steps that we think are necessary for identifying consciousness in other creatures. Step 1 is to define the particular kind of consciousness in which one is interested. Step 2 is to identify, in humans, the key differences between the brain processes that are associated with consciousness and the brain processes that are not associated with consciousness. For Step 2, to identify these differences, we focus on passive frame theory. Step 3 concerns how the insights derived from consciousness research on humans (e.g., concerning these differences) can be generalized to other creatures. We discuss the significance of examining how consciousness was fashioned by the process of evolution, a process that could be happenstance and replete with incessant tinkering, yielding adaptations that can be suboptimal and counterintuitive, far different in nature from our efficiently designed robotic systems. We conclude that the more that is understood about the differences between conscious processing and unconscious processing in humans, the easier it will be to identify consciousness in other creatures. Full article

In disorders of consciousness, verticalization is considered an effective type of treatment to improve motor and cognitive recovery. Our purpose is to investigate neurophysiological effects of robotic verticalization training (RVT) in patients with minimally conscious state (MCS). Thirty subjects affected by MCS due to traumatic or vascular brain injury, attending the intensive Neurorehabilitation Unit of the IRCCS Neurolesi (Messina, Italy), were included in this retrospective study. They were equally divided into two groups: the control group (CG) received traditional verticalization with a static bed and the experimental group (EG) received advanced robotic verticalization using the Erigo device. Each patient was evaluated using both clinical scales, including Levels of Cognitive Functioning (LCF) and Functional Independence Measure (FIM), and quantitative EEG pre (T0) and post each treatment (T1). The treatment lasted for eight consecutive weeks, and sessions were held three times a week, in addition to standard neurorehabilitation. In addition to a notable improvement in clinical parameters, such as functional (FIM) (p < 0.01) and cognitive (LCF) (p < 0.01) outcomes, our findings showed a significant modification in alpha and beta bands post-intervention, underscoring the promising effect of the Erigo device to influence neural plasticity and indicating a noteworthy difference between pre-post intervention. This was not observed in the CG. The observed changes in alpha and beta bands underscore the potential of the Erigo device to induce neural plasticity. The device’s custom features and programming, tailored to individual patient needs, may contribute to its unique impact on brain responses. Full article

The human mind can be thought of as a black box, where the external inputs are elaborated in an unknown way and lead to external outputs. D’Ariano and Faggin schematized thinking and consciousness through quantum state dynamics. The complexity of mental states can be formalized through the entanglement of the so-called qualia states. Thus, the interaction between the mind and the external world can be formalized as an interplay between classical and quantum-state dynamics. Since quantum computing is more and more often being applied to robots, and robots constitute a benchmark to test schematic models of behavior, we propose a case study with a robotic dance, where the thinking and moving mechanisms are modeled according to quantum–classic decision making. In our research, to model the elaboration of multi-sensory stimuli and the following decision making in terms of movement response, we adopt the D’Ariano–Faggin formalism and propose a case study with improvised dance based on a collection of poses, whose combination is presented in response to external and periodic multi-sensory stimuli. We model the dancer’s inner state and reaction to classic stimuli through a quantum circuit. We present our preliminary results, discussing further lines of development. Full article

One of the most puzzling questions in neuroscience is the nature of emotions and their role in consciousness. The brain’s significant energy investment in maintaining the resting state indicates its essential role as the ground state of consciousness, the source of the sense of self. Emotions, the brain’s homeostatic master regulators, continuously measure and motivate the recovery of the psychological equilibrium. Moreover, perception’s information-energy exchange with the environment gives rise to a closed thermodynamic cycle, the reversible Carnot engine. The Carnot cycle forms an exothermic process; low entropy and reversible resting state turn the focus to the past, causing regret and remorse. The endothermic reversed Carnot cycle creates a high entropy resting state with irreversible activations generating novelty and intellect. We propose that the cycle’s direction represents psychological spin, where the endothermic cycle’s energy accumulation forms up-spin, and the energy-wasting exothermic cycle represents down-spin. Psychological spin corresponds to attitude, the determining factor in cognitive function and social life. By applying the Pauli exclusion principle for consciousness, we can explain the need for personal space and the formation of hierarchical social structures and animals’ territorial needs. Improving intuition about the brain’s intelligent computations may allow new treatments for mental diseases and novel applications in robotics and artificial intelligence. Full article

Consciousness has been described as acting as a global workspace that integrates perception, imagination, emotion and action programming for adaptive decision making. The mechanisms of this workspace and their relationships to the phenomenology of consciousness need to be further specified. Much research in this area has focused on the neural correlates of consciousness, but, arguably, computational modeling can better be used toward this aim. According to the Projective Consciousness Model (PCM), consciousness is structured as a viewpoint-organized, internal space, relying on 3D projective geometry and governed by the action of the Projective Group as part of a process of active inference. The geometry induces a group-structured subjective perspective on an encoded world model, enabling adaptive perspective taking in agents. Here, we review and discuss the PCM. We emphasize the role of projective mechanisms in perception and the appraisal of affective and epistemic values as tied to the motivation of action, under an optimization process of Free Energy minimization, or more generally stochastic optimal control. We discuss how these mechanisms enable us to model and simulate group-structured drives in the context of social cognition and to understand the mechanisms underpinning empathy, emotion expression and regulation, and approach–avoidance behaviors. We review previous results, drawing on applications in robotics and virtual humans. We briefly discuss future axes of research relating to applications of the model to simulation- and model-based behavioral science, geometrically structured artificial neural networks, the relevance of the approach for explainable AI and human–machine interactions, and the study of the neural correlates of consciousness. Full article

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind of possibility by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is. Full article

In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines. Full article

In this paper, the human-like motion issue for anthropomorphic arms is further discussed. An Intelligent Human-like Motion Planner (IHMP) consisting of Movement Primitive (MP), Bayesian Network (BN) and Coupling Neural Network (CPNN) is proposed to help the robot generate human-like arm movements. Firstly, the arm motion model is decoupled in the aspects of arm structure and motion process, respectively. In the former aspect, the arm model is decoupled into different simple models through the Movement Primitive. A Hierarchical Planning Strategy (HPS) is proposed to decouple a complete motion process into different sub-processes. Based on diversified arm motion models, the Bayesian Network is used to help the robot choose the suitable motion model among these arm motion models. Then, according to the features of diversified arm motion models, the Coupling Neural Network is proposed to obtain the inverse kinematic (IK) solutions. This network can integrate different models into a single network and reflect the features of these models by changing the network structure. Being a major contribution to this paper, specific focus is on the improvement of human-like motion accuracy and independent consciousness of robots. Finally, the availability of the IHMP is verified by experiments on a humanoid robot Pepper. Full article

Background: Music stimulation is considered a valuable form of intervention in disorders of consciousness (DoC); for instance, verticalization may improve motor and cognitive recovery. Our purpose is to investigate the effects of a novel rehabilitative approach combining robotic verticalization training (RVT) with personalized music stimulation in people with DoC. Methods: Sixteen subjects affected by minimally conscious state due to traumatic brain lesions who attended our Intensive Neuro-Rehabilitation Unit were enrolled in this randomized trial. They received either music robotic verticalization (MRV) using the Erigo device plus a personalized music playlist or only RVT without music stimuli. Each treatment was performed 2 times a week for 8 consecutive weeks in addition to standard neurorehabilitation. Results: We found significant improvements in all patients’ outcomes in the experimental group (who received MRV): Coma Recovery Scale-Revised (CRS-R) (p < 0.01), Level of Cognitive Functioning (LCF) (p < 0.02), Functional Independence Measure (FIM) (p < 0.03), Functional Communication Scale (FCS) (p < 0.007), Trunk Control Test (TCT) (p = 0.05). Significant differences between the two groups were also found in the main outcome measure CRS-R (p < 0.01) but not for TCT and FIM. Conclusions: Our study supports the safety and effectiveness of RVT with the Erigo device in chronic MCS, and the achievement of better outcomes when RVT is combined with music stimulation. Full article

The philosophy of AI currently comprises the core of Philosophy and Computing. Fascinating ideas include: B. Goertzel states that humans use paraconsistent logic, which robots should follow; S. Thaler’s discovery engines (DABUS), which are at the early stage AGI; the Uplift team trying to build advanced AI around the kernel of ethics, while solving the problem of life-long-learning neural nets. J. Copeland gave an IS4SI plenary, while Oron Shagrir and Jun Tani gave APC plenary lectures. J. Bach assembled an amazing panel on machine consciousness. Novel session on the fourth space was led by D. Hardegger. Invited speakers including M. Burgin, S. Castro, R. Goodwin, R. Manzotti, M. Talanov. Graduate presentations e.g., J. Dakowski, C. Abels. BICA/APC panel on ethics and future AGI with R. Yampolkiy, M. Waser and D. Kelley. Full article