4/9/2023 0 Comments Musicality in dance![]() The work presented here proposes solutions towards this direction. The long-term aim of the project is to endow humanoid robots with the necessary haptic skills to perform collaborative tasks with a human operator as a partner rather than as a helper. Therefore, this thesis will focus on the kinesthetic aspects of collaborative tasks. ![]() ![]() In the context of physical tasks, where the partners are in direct or indirect contact through an object and exchange mechanical energy, we believe that the haptic channel is a favored and fast way for the partners to exchange information about their intentions. In some recent works, robots were sometimes even given the possibility to lead human operators. Recently, thanks to the increasing computational power embedded into the robots, more and more initiative has been given to robotic assistants. Because robotic systems have limited cognitive capabilities in comparison to human beings, a follower role has generally been assigned to robotic systems to cooperate with human operators. This model has often been used in physical Human-Robot Interaction (pHRI). To avoid conflicts among the partners' intentions, the leader-follower model defines a task leader, who imposes a task plan to the other partners, while the latter act as follower and follow at best the intentions of the leader. When jointly manipulating an object, the partners no longer act independently, and must negotiate a common plan to perform the task. Human beings learn naturally how to perform such tasks with other human partners, but implementing such behaviors on a robotic platform is challenging. ![]() A typical example of such tasks are collaborative manipulation tasks, where the partners apply forces on a same object to impose it a desired motion or bring it to a target location. Haptic collaborative tasks are actions performed jointly by several partners, involving direct or indirect physical contact among them. This review analyses the literature related to five general areas: (I) breakthrough studies on the mirror system, and subsequent studies on its involvement in the prediction, the execution, the control of movement, and in the process of “embodied simulation” within the intersubjective relationship (II) research focused on investigating the neural networks in action observation, and the neural correlates of motor expertise highlighted by comparative studies on different dance styles (III) studies dealing with the viewer's experience of dance according to specific dance repertoires, which revealed the relevance of choreographic choices for aesthetic appreciation (IV) studies focused on dance as an aesthetic experience, where both the emotional and the cultural dimension play a significant role, and whose investigation paves the way to further progress both in empirical and in phenomenological research methodologies (V) collaboration-based experiments, in which neuroscientists and choreographers developed expertise-related questions, especially focusing on the multiple phenomena that underlie motor imagery. Neuroscience has analyzed the mirror system through neuroimaging techniques, testing its role in imitative learning, in the recognition of other people's emotions and especially in the understanding of the motor behavior of others. The aim of this review is to highlight the most relevant contributions on dance in neuroscientific research. This work shows the potential that teleoperation holds for transmitting both dynamic and communicative information on the task, which classical methods for programming by demonstration have traditionally overlooked. The proposed learning approach is generative and can be used to drive the robot's retrieval of the task by ensuring a faithful reproduction of the overall dynamics of the task, namely by reproducing the force patterns for both lift the object and adapt to the human user's hand motion. ![]() We show that the proposed statistical model can efficiently encapsulate typical communication patterns across different dyads of users, that are stereotypical of collaborative behaviours between humans and robots. Haptic communication reflects more than pure dynamic information on the task, and includes communication patterns, which result from the two users constantly adapting their hand motion to coordinate in time and space their respective motions. Learning extracts statistical redundancies in the examples provided during training by using Gaussian Mixture Regression and Hidden Markov Model. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. ![]()
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