20 research outputs found

    Artificial self-awareness for robots

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    Robots are evolving and entering into various sectors and aspects of life. While humans are aware of their bodies and capabilities, which help them work on a task in different environments, robots are not. This thesis is about defining and developing a robotic artificial self-awareness framework. The aim is to allow robots to adapt to their environment and better manage their task. The robot’s artificial self-aware knowledge is captured based on levels where each level helps a robot acquire higher self-awareness competence. These levels are inspired by Rochat [1] self-awareness development levels in humans, where each level is associated with a complexity of self-knowledge. Self-awareness in humans leads to distinguishing themselves from the environment, allowing humans to understand themselves and control their capabilities. This work focuses on the first and second levels of self awareness through differentiation and situation (minimal self). The artificial self-awareness level-1 proposes the first step towards a basic, minimal self-awareness in a robot. The artificial self-awareness level-2 proposes an increasing capacity of self-awareness knowledge in the robot. That is, this thesis posits an experimental methodology to evaluate whether the robot can differentiate and situate itself from the environment and to test whether artificial self-awareness level-1 and level-2 increase a robot’s self-certainty in an unseen environment. The research utilises deep neural network techniques to allow a dual-arm robot to identify itself within different environments. The robot vision and proprioception are captured using a camera and robot sensors to build a model that allows a robot to differentiate itself from the environment. The level-1 results indicate that a robot can distinguish itself with an accuracy of 80.3% on average in different environmental settings and under confounding input signals. Also, the level-2 results show that a robot can situate itself in different environments with an accuracy of 86.01% yielding a higher artificial self-certainty of 5.71%. This thesis work helps a robot be aware of itself in different environments

    Laterality and Babble: Does asymmetry in lip opening during babble indicate increasing left hemisphere dominance as babies gain articulatory experience?

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    Speech and language are supported by task-dependent neural networks that are predominantly lateralised to the left hemisphere of the brain, whilst emotion is supported by predominantly right hemispheric networks. This is reflected in the asymmetry of lip openings during speech and facial expressions in adults. One cross-sectional orofacial asymmetry study found an analogous distinction between 5-12-month-old babies’ lip openings during reduplicated babble and during positively valenced emotional facial expressions and this has been interpreted as evidence to support the hypothesis that babble is fundamentally linguistic in nature (Holowka & Petitto, 2002). However, a similar distinction is also observed in orofacial behaviours in some non-human primates. Differential hemispheric specialisation for emotional and vocal communicative functions may then be an ancient trait, long predating human language. Additionally, characterising babble as babies’ immature attempts to do language marginalises the critical role of endogenously motivated vocal exploration and may assume a degree of goal-directedness in infant behaviour around the time of babble emergence for which we have little other supporting evidence. This thesis explores laterality in eight 5-12-month-old’s babble, positive facial expressions, and other vocalisations longitudinally. Singleton and variegated babble are captured as well as reduplicated babble, and an alternative method for analysing orofacial asymmetry – hemimouth measurement – is used. Overall, Holowka and Petitto’s between-category distinction was replicated. However, babble was found to show right laterality at emergence and become left lateralised gradually over developmental time. Some interactional effect of utterance complexity was also observed. Bisyllabic babbles showed significant leftward shift over developmental time, whilst monosyllabic and polysyllabic babbles did not. Furthermore, hemimouth measurement revealed a degree of real-time variability in the laterality of babble not previously observed. An alternative theory of the underlying nature of babble – the Old Parts, New Machine hypothesis – is proposed

    Sensorimotor representation learning for an "active self" in robots: A model survey

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    Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyse what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration

    Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey

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    Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Projekt DEALPeer Reviewe

    Vocal Imitation in Sensorimotor Learning Models: a Comparative Review

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    International audienceSensorimotor learning represents a challenging problem for natural and artificial systems. Several computational models have been proposed to explain the neural and cognitive mechanisms at play in the brain. In general, these models can be decomposed in three common components: a sensory system, a motor control device and a learning framework. The latter includes the architecture, the learning rule or optimisation method, and the exploration strategy used to guide learning. In this review, we focus on imitative vocal learning, that is exemplified in song learning in birds and speech acquisition in humans. We aim to synthesise, analyse and compare the various models of vocal learning that have been proposed, highlighting their common points and differences. We first introduce the biological context, including the behavioural and physiological hallmarks of vocal learning and sketch the neural circuits involved. Then, we detail the different components of a vocal learning model and how they are implemented in the reviewed models

    Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

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    abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201
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