| Field: Neural Robotics | |
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Hybrid human-neurorobotics approach to primary intersubjectivity via active inferenceVisit the project: Github repository |
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Interdisciplinary efforts from developmental psychology, phenomenology, and philosophy of mind, have studied the rudiments of social cognition and conceptualized distinct forms of intersubjective communication and interaction at human early life. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We study primary intersubjectivity as a second person perspective experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. Hence, from the perspective of neurorobotics and our interpretation of the concept of active inference in free-energy principle theory, we investigate the relation between intentionality, motor compliance, cognitive compliance, and behavior emergence; within a PVRNN neural network model. Our experiments with the humanoid Torobo portrait interesting perspectives for the bio-inspired study of developmental and social processes, resulting in potential applications in the fields of educational technology and cognitive rehabilitation, among others.
| Field: Psychology | |
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A dynamic computational model of motivation based on self-determination theory and CANNVisit the project: Github repository |
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The hierarchical model of intrinsic and extrinsic motivation (HMIEM) is a framework based on the principles of self-determination theory (SDT) which describes human motivation from a multilevel perspective, and integrates knowledge on personality and social psychological determinants of motivation and its consequences. Although over the last decades HMIEM has grounded numerous correlational studies in diverse fields, it is conceptually defined as a schematic representation of the dynamics of motivation, that is not suitable for human and artificial agents research based on tracking. In this work we propose an analytic description named dynamic computational model of motivation (DCMM), inspired by HMIEM and based on continuous attractor neural networks, which consists in a computational framework of motivation. In DCMM the motivation state is represented within a self-determination continuum with recurrent feedback connections, receiving inputs from heterogeneous layers. Through simulations we show the modeling of complete scenarios in DCMM. A field study with faculty subjects illustrates how DCMM can be provided with data from SDT constructs observations. We believe that DCMM is relevant for investigating unresolved issues in HMIEM, and potentially interesting to related fields, including psychology, artificial intelligence, behavioral and developmental robotics, and educational technology.
| Field: Underwater Robotics | |
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Neural network for black-box fusion of underwater robot localization under unmodeled noiseVisit the project: Github repository |
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The research on autonomous robotics has focused on the aspect of information fusion from redundant estimates. Choosing a convenient fusion policy, that reduces the impact of unmodeled noise, and is computationally efficient, is an open research issue. The objective of this work is to study the problem of underwater localization which is a challenging field of research, given the dynamic aspect of the environment. For this, we explore navigation task scenarios based on inertial and geophysical sensory. We propose a neural network framework named B-PR-F which heuristically performs adaptable fusion of information, based on the principle of contextual anticipation of the localization signal within an ordered processing neighborhood. In the framework black-box unimodal estimations are related to the task context, and the confidence on individual estimates is evaluated before fusing information. A study conducted in a virtual environment illustrates the relevance of the model in fusing information under multiple task scenarios. A real experiment shows that our model outperforms the Kalman Filter and the Augmented Monte Carlo Localization algorithms in the task. We believe that the principle proposed can be relevant to related application fields, involving the problem of state estimation from the fusion of redundant information.
| Field: Cognitive Robotics | |
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Grounding Humanoid Visually Guided Walking: From Action-independent to Action-oriented KnowledgeRead the articles: Information Sciences |
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In the context of humanoid and service robotics, it is essential that the agent can be positioned with respect to objects of interest in the environment. By relying mostly on the cognitivist conception in artificial intelligence, the research on visually guided walking has tended to overlook the characteristics of the context in which behavior occurs. Consequently, considerable efforts have been directed to define action-independent explicit models of the solution, often resulting in high computational requirements. In this study, inspired by the embodied cognition research, our interest has focused on the analysis of the sensory-motor coupling. Notably, on the relation between embodiment, information, and action-oriented representation. Hence, by mimicking human walking, a behavior scheme is proposed and endowed the agent with the skill of approaching stimuli. A significant contribution to object discrimination was obtained by proposing an efficient visual attention mechanism, that exploits the redundancies and the statistical regularities induced in the sensory-motor coordination, thus the information flow is anticipated from the fusion of visual and proprioceptive features in a Bayesian network. The solution was implemented on the humanoid platform Nao, where the task was accomplished in an unstructured scenario.
