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Subspace clustering algorithms are used for understanding the cluster structure that explains the patterns prevalent in the dataset well. These methods are extensively used for data-exploration tasks in various areas of Natural Sciences. However, most of these methods fail to handle confounding attributes in the dataset. For datasets where a data sample represent multiple attributes, naively applying any clustering approach can result in undesired output. To this end, we propose a novel framework for jointly removing confounding attributes while learning to cluster data points in individual subspaces. Assuming we have label information about these confounding attributes, we regularize the clustering method by adversarially learning to minimize the mutual information between the data representation and the confounding attribute labels. Our experimental result on synthetic and real-world datasets demonstrate the effectiveness of our approach. 

In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows for model adaptation when new operation conditions come into play or the physics-based model is insufficient (or incomplete) to properly describe the latent phenomenon. One advantage of the APBMs and our estimation procedure is the capability of maintaining the physical interpretability of estimated states. Furthermore, we propose a constraint filtering approach to control the neural network contributions to the overall model. We also exploit assumed density filtering techniques and cubature integration rules to present a flexible estimation strategy that can easily deal with nonlinear models and high-dimensional latent spaces. Finally, we demonstrate the efficacy of our methodology by leveraging a target tracking scenario with nonlinear and incomplete measurement and acceleration models, respectively. 

The effectiveness of human-robot interactions critically depends on the success of computational efforts to emulate human inference of intent, anticipation of action, and coordination of movement. To this end, we developed two models that leverage a well described feature of human movement: Gaussian-shaped submovements in velocity profiles, to act as robotic surrogates for human inference and trajectory planning in a handover task. We evaluated both models based on how early in a handover movement the inference model can obtain accurate estimates of handover location and timing, and how similar model trajectories are to human receiver trajectories. Initial results using one participant dyad demonstrate that our inference model can accurately predict location and handover timing, while the trajectory planner can use these predictions to provide a human-like trajectory plan for the robot. This approach delivers promising performance while remaining grounded in physiologically meaningful Gaussian-shaped velocity profiles of human motion.