COSM2IC: Optimizing real-time multi-modal instruction comprehension

• Main Authors: WEERAKOON MUDIYANSELAGE DULANGA KAVEESHA WEERAKOON, SUBBARAJU, Vigneshwaran, TRAN, Minh Anh Tuan, MISRA, Archan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Deep Learning for Visual Perception; Data Sets for Robotic Vision; Embedded Systems for Robotic andAutomation; Human-Robot Collaboration; RGB-D Perception; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7618; https://ink.library.smu.edu.sg/context/sis_research/article/8621/viewcontent/iros_final.pdf
• Institution: Singapore Management University

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.