To automatically estimate the aesthetics of robotic dance poses, the following ten machine learning methods are deployed: Naive Bayes, Bayesian logistic regression, SVM, RBF network, ADTree, random forest, voted perceptron, KStar, DTNB, and bagging. The above two categories of features are fused to portray completely a robotic dance pose. From the visual channel, the shape features (including eccentricity, density, rectangularity, aspect ratio, Hu-moment Invariants, and complex coordinate based Fourier descriptors) are extracted from an image from the non-visual channel, joint motion features are obtained from the internal kinestate of a robot. Therefore, we propose a novel automatic approach to estimate the aesthetics of robotic dance poses by fusing multimodal information.
Similarly, if a robot could perceive the aesthetics of its own dance poses, the robot could demonstrate more autonomous and humanoid behavior during robotic dance creation. Human dancers in front of mirrors estimate the aesthetics of their own dance poses by fusing multimodal information (visual and non-visual) to improve their dancing performances. The experimental results verify the effectiveness of the proposed algorithms.Īesthetic ability is an advanced cognitive function of human beings.
A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user’s falling mode when he/she is in an abnormal walking state. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user’s upper and lower limbs. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user’s normal walking state and abnormal walking state for a walking-aid robot system. Overall, our results suggest that robots could successfully engage older adults in partner dance-based exercise. Throughout the study, our robot used admittance control to successfully dance with older adults, demonstrating the feasibility of this method. Through a qualitative data analysis of structured interview data, we also identified facilitators and barriers to acceptance of robots for partner dance-based exercise. Participants tended to perceive the robot as easier to use after performing the PST with it. According to questionnaires, participants were generally accepting of the robot for partner dance-based exercise, tending to perceive it as useful, easy to use, and enjoyable. Participants led the robot by maintaining physical contact and applying forces to the robot’s end effectors. Participants successfully led a human-scale wheeled robot with arms (i.e., a mobile manipulator) in a simple, which we refer to as the Partnered Stepping Task (PST). Using methods from the technology acceptance literature, we conducted a study with 16 healthy older adults to investigate their acceptance of robots for partner dance-based exercise. However, partner dance involves physical contact between the dancers, and older adults would need to be accepting of partner dancing with a robot. Robots could potentially facilitate healthy aging by engaging older adults in partner dance-based exercise. Partner dance has been shown to be beneficial for the health of older adults. The results showed that the robot learnt, using interactive reinforcement learning, the preferences of human partners, and the dance improved with the extracted preferences from more human partners. Together with Softmax action-selection method, the Sarsa reinforcement learning algorithm was used as the underlining learning algorithm and to effectively control the trade-off between exploitation of the learnt dance skills and exploration of new dance actions. performing more of what was preferred and less of that was not preferred. The extracted preferred dance actions from different people were then combined to generate improved dance sequences, i.e. By using a buffering technique to store the dance actions before a feedback, each individual’s preferences can be extracted even when a reward is received late. Human’s preferences were extracted by analysing the common action patterns with positive or negative feedback from the human during robot dancing. In this paper, we investigated an approach for robots to learn to adapt dance actions to human’s preferences through interaction and feedback.
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