Cosm2ic: Optimizing real-time multi-modal instruction comprehension

Supporting real-time, on-device execution of multimodal referring instruction comprehension models is an important challenge to be tackled in embodied Human-Robot Interaction.However, state-of-the-art deep learning models are resourceintensive and unsuitable for real-time execution on embedded devices. While model compression can achieve a reduction in computational resources up to a certain point, further optimizations result in a severe drop in accuracy. To minimize this loss in accuracy, we propose the COSM2IC framework, with a lightweight Task Complexity Predictor, that uses multiple sensor inputs to assess the instructional complexity and thereby dynamically switch between a set of models of varying computational intensity such that computationally less demanding models are invoked whenever possible. To demonstrate the benefits of COSM2IC, we utilize a representative human-robot collaborative “table-top target acquisition” task, to curate a new multimodal instruction dataset where a human issues instructions in a natural manner using a combination of visual, verbal, and gestural (pointing) cues. We show that COSM2IC achieves a 3-fold reduction in comprehension latency when compared to a baseline DNN model while suffering an accuracy loss of only ∼5%. When compared to state-of-the-art model compression methods, COSM2IC is able to achieve a further 30% reduction in latency and energy consumption for a comparable performance.

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