Learning instance-level N-ary semantic knowledge at scale for robots operating in everyday environments


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Robots operating in everyday environments need to effectively perceive, model, and infer semantic properties of objects. Existing knowledge reasoning frameworks only model binary relations between an object’s class label and its semantic properties, unable to collectively reason about object properties detected by different perception algorithms and grounded in diverse sensory modalities. We bridge the gap between multimodal perception and knowledge reasoning by introducing an n-ary representation that models complex, inter-related object properties. To tackle the problem of collecting n-ary semantic knowledge at scale, we propose transformer neural networks that generalize knowledge from observations of object instances by learning to predict single missing properties or predict joint probabilities of all properties. The learned models can reason at different levels of abstraction, effectively predicting unknown properties of objects in different environmental contexts given different amounts of observed information. We quantitatively validate our approach against prior methods on LINK, a unique dataset we contribute that contains 1457 object instances in different situations, amounting to 15 multimodal properties types and 200 total properties. Compared to the top-performing baseline, a Markov Logic Network, our models obtain a 10% improvement in predicting unknown properties of novel object instances while reducing training and inference time by more than 150 times. Additionally, we apply our work to a mobile manipulation robot, demonstrating its ability to leverage n-ary reasoning to retrieve objects and actively detect object properties. The code and data are available at https://github.com/wliu88/LINK .
Semantic reasoning,N-ary relation,Object-centric reasoning,Transformer networks
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