Gestures serve as a fundamental and significant mode of non-verbal communication among humans. Deictic gestures (such as pointing towards an object), in particular, offer valuable means of efficiently expressing intent in situations where language is inaccessible, restricted, or highly specialized. As a result, it is essential for robots to comprehend gestures in order to infer human intentions and establish more effective coordination with them. Prior work often rely on a rigid hand-coded library of gestures along with their meanings. However, interpretation of gestures is often context-dependent, requiring more flexibility and common-sense reasoning. In this work, we propose a framework, GIRAF, for more flexibly interpreting gesture and language instructions by leveraging the power of large language models. Our framework is able to accurately infer human intent and contextualize the meaning of their gestures for more effective human-robot collaboration. We instantiate the framework for interpreting deictic gestures in table-top manipulation tasks and demonstrate that it is both effective and preferred by users, achieving 70% higher success rates than the baseline. We further demonstrate GIRAF's ability on reasoning about diverse types of gestures by curating a GestureInstruct dataset consisting of 36 different task scenarios. GIRAF achieved 81% success rate on finding the correct plan for tasks in GestureInstruct. Website: https://tinyurl.com/giraf23

The popular approach is to constrain the reconstructions using compactness priors including sparsity. Several researchers have recently introduced off-the-grid continuous domain priors that are robust to discretization errors [1, 2], which provide significantly improved image quality in a range of applications. However, the main challenge is the significant increase in computational complexity. Recently, several researchers have introduced deep learning methodsas fast and efficient alternatives to compressed sensing algorithms. Current approaches can be categorized into direct and model based strategies. The direct approaches directly estimate the images from the undersampled measurements ortheir transforms/features [3, 4]. These methods learn to invert the forward operator over the space/manifold of images. Whilethis approach is more popular, a challenge with these schemes is the need to learn the inverse, which often requires large models (e.g.