Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2022

Abstract

Supporting real-time, on-device execution of multi-modal 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 resource-intensive 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 multi-modal 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.

Keywords

Deep Learning for Visual Perception, Data Sets for Robotic Vision, Embedded Systems for Robotic andAutomation, Human-Robot Collaboration, RGB-D Perception;

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Robotics and Automation Letters

Volume

7

Issue

4

First Page

10697

Last Page

10704

ISSN

2377-3766

Identifier

10.1109/LRA.2022.3194683

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/LRA.2022.3194683

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