A painting is more than just a picture on a wall; a painting is a process comprised of many intentional brush strokes, the shapes of which are an important component of a painting's overall style and message. Prior work in modeling brush stroke trajectories either does not work with real-world robotics or is not flexible enough to capture the complexity of human-made brush strokes. In this work, we introduce Spline-FRIDA which can model complex human brush stroke trajectories. This is achieved by recording artists drawing using motion capture, modeling the extracted trajectories with an autoencoder, and introducing a novel brush stroke dynamics model to the existing robotic painting platform FRIDA. We conducted a survey and found that our open-source Spline-FRIDA approach successfully captures the stroke styles in human drawings and that Spline-FRIDA's brush strokes are more human-like, improve semantic planning, and are more artistic compared to existing robot painting systems with restrictive B\'ezier curve strokes.

In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level control policies and higher-level policies of how to use them from demonstrations of a robot performing various tasks. By representing options as continuous latent variables, we frame the problem of learning these options as latent variable inference. We then present a temporal formulation of variational inference based on a temporal factorization of trajectory likelihoods,that allows us to infer options in an unsupervised manner. We demonstrate the ability of our framework to learn such options across three robotic demonstration datasets.

We address the problem of spatial segmentation of a 2D object in the context of a robotic system for painting, where an optimal segmentation depends on both the appearance of the object and the size of each segment. Since each segment must take into account appearance features at several scales, we take a hierarchical grammar-based parsing approach to decompose the object into 2D segments for painting. Since there are many ways to segment an object the solution space is extremely large and it is very challenging to utilize an exploration based optimization approach like reinforcement learning. Instead, we pose the segmentation problem as an imitation learning problem by using a segmentation algorithm in the place of an expert, that has access to a small dataset with known foreground-background segmentations. During the imitation learning process, we learn to imitate the oracle (segmentation algorithm) using only the image of the object, without the use of the known foreground-background segmentations. We introduce a novel deterministic policy gradient update, DRAG, in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural network based object parser. We will also show that our approach can be seen as extending DDPG to the Imitation Learning scenario. Training our neural parser to imitate the oracle via DRAG allow our neural parser to outperform several existing imitation learning approaches.