For many tasks, tactile or visual feedback is helpful or even crucial. However, designing controllers that take such high-dimensional feedback into account is non-trivial. Therefore, robots should be able to learn tactile skills through trial and error by using reinforcement learning algorithms. The input domain for such tasks, however, might include strongly correlated or non-relevant dimensions, making it hard to specify a suitable metric on such domains. Auto-encoders specialize in finding compact representations, where defining such a metric is likely to be easier. Therefore, we propose a reinforcement learning algorithm that can learn non-linear policies in continuous state spaces, which leverages representations learned using auto-encoders. We first evaluate this method on a simulated toytask with visual input. Then, we validate our approach on a real-robot tactile stabilization task.
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity. Current approaches are limited to low-dimensional latent spaces, due to the computational complexity of solving a non-convex optimisation problem. We propose finding shortest paths in a finite graph of samples from the aggregate approximate posterior, that can be solved exactly, at greatly reduced runtime, and without a notable loss in quality. Our approach, therefore, is hence applicable to high-dimensional problems, e.g., in the visual domain. We validate our approach empirically on a series of experiments using variational autoencoders applied to image data, including the Chair, FashionMNIST, and human movement data sets.
Abstract-Estimating human fingertip forces is required to understand force distribution in grasping and manipulation. Human grasping behavior can then be used to develop forceand impedance-based grasping and manipulation strategies for robotic hands. However, estimating human grip force naturally is only possible with instrumented objects or unnatural gloves, thus greatly limiting the type of objects used.In this paper we describe an approach which uses images of the human fingertip to reconstruct grip force and torque at the finger. Our approach does not use finger-mounted equipment, but instead a steady camera observing the fingers of the hand from a distance. This allows for finger force estimation without any physical interference with the hand or object itself, and is therefore universally applicable.We construct a 3-dimensional finger model from 2D images. Convolutional Neural Networks (CNN) are used to predict the 2D image to a 3D model transformation matrix. Two methods of CNN are designed for separate and combined outputs of orientation and position. After learning, our system shows an alignment accuracy over 98% on unknown data.In the final step, a Gaussian process estimates finger force and torque from the aligned images based on color changes and deformations of the nail and its surrounding skin. Experimental results shows that the accuracy achieves about 95% in the force estimation and 90% in the torque.
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