In this paper we present an approach and a benchmark for visual reasoning in robotics applications, in particular small object grasping and manipulation. The approach and benchmark are focused on inferring object properties from visual and text data. It concerns small household objects with their properties, functionality, natural language descriptions as well as question-answer pairs for visual reasoning queries along with their corresponding scene semantic representations. We also present a method for generating synthetic data which allows to extend the benchmark to other objects or scenes and propose an evaluation protocol that is more challenging than in the existing datasets.We propose a reasoning system based on symbolic program execution. A disentangled representation of the visual and textual inputs is obtained and used to execute symbolic programs that represent a 'reasoning process' of the algorithm. We perform a set of experiments on the proposed benchmark and compare to results from the state of the art methods. These results expose the shortcomings of the existing benchmarks that may lead to misleading conclusions on the actual performance of the visual reasoning systems.

No abstract

No abstract

No abstract

Humans exhibit incredibly high levels of multi-modal understanding -combining visual cues with read, or heard knowledge comes easy to us and allows for very accurate interaction with the surrounding environment. Various simulation environments focus on providing data for tasks related to scene understanding, question answering, space exploration, visual navigation. In this work, we are providing a solution to encompass both, visual and behavioural aspects of simulation in a new environment for learning interactive reasoning in manipulation setup. SAMPLE-HD environment allows to generate various scenes composed of small household objects, to procedurally generate language instructions for manipulation, and to generate ground truth paths serving as training data.