Learning Neuro-symbolic Programs for Language Guided Robot Manipulation

Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this task possess one of the following limitations: (i) rely on hand-coded symbols for concepts limiting generalization beyond those seen during training [1] (ii) infer action sequences from instructions but require dense sub-goal supervision [2] or (iii) lack semantics required for deeper object-centric reasoning inherent in interpreting complex instructions [3]. In contrast, our approach can handle linguistic as well as perceptual variations, end-to-end trainable and requires no intermediate supervision. The proposed model uses symbolic reasoning constructs that operate on a latent neural object-centric representation, allowing for deeper reasoning over the input scene. Central to our approach is a modular structure consisting of a hierarchical instruction parser and an action simulator to learn disentangled action representations. Our experiments on a simulated environment with a 7-DOF manipulator, consisting of instructions with varying number of steps and scenes with different number of objects, demonstrate that our model is robust to such variations and significantly outperforms baselines, particularly in the generalization settings. The code, dataset and experiment videos are available at https://nsrmp.github.ioComment: International Conference on Robotics and Automation (ICRA), 202

Making Fractional Distances work in the presence of White Noise - Motivation and Challenges

In Similarity Search (SS), given a new piece of data (or a query), often a close enough match to it from a given set of data points is sought. One view of SS is that the query is assumed to be a noise corrupted data point. In line with this view, François et. al. [1] argue that the Euclidean norm and fractional distances give better search results in the case of white noise and highly coloured noise, respectively. Further, Singh and Jayaram [2] showed that the fractional distances work well even when the noise is not-so-highly coloured. In this work, we attempt to determine if fractional distances could be made to work in the setting of SS even when the noise is white. The real challenges lie in the many counter-intuitive phenomena in high dimensional spaces which will be discussed briefly and our approach in tackling them will also be discussed in this work. © 2022 Owner/Author