SRIS Module · HIC

Human-centered Interactive Control

interpretable and verifiable human–robot interaction control theory and methods that integrate multimodal intention understanding, shared autonomy, and safe interactive control for human–robot collaboration in dynamic and uncertain environments.

Time Series Scalable Learning Sequence Mining
01
Long Horizon
02
Online Processing
03
Pattern Discovery
SRIS Laboratory SCUT · GZIC
Smart, Reliable, and Interpretable Systems
Overview

Research scope

The SIRS Lab studies human-centered interactive control for intelligent robotic systems in dynamic and uncertain environments. It aims to integrate multimodal intention understanding, adaptive shared autonomy, and safety-assured physical interaction into a unified framework for responsive, trustworthy, and personalized human–robot collaboration. The lab further emphasizes interpretable and formally grounded methods that connect human intent and task semantics with low-level control.

Multimodal Human–Robot Interaction Shared Autonomy Safe Interactive Control
Three key scientific problems:
  • How can multimodal human signals be transformed into a unified control-oriented representation of intention, capability, and context for real-time interaction
  • How can shared autonomy be designed to dynamically allocate control authority while preserving safety, stability, performance, and human agency
  • How can high-level semantic intelligence be grounded into low-level embodied control with guarantees on interpretability, robustness, and adaptation.
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    Formal Interpretation of Cyber-Physical System Performance with Temporal Logic

    Gang Chen, Zachary Sabato, Zhaodan Kong
    Cyber-Physical Systems, 4(3), 175-203
    Journal
    We propose a formal interpretation framework that allows a human user to interrogate a cyber-physical system using temporal logic queries. The method formulates interpretation as a temporal logic inference problem and introduces a Gaussian-process-based active learning algorithm to obtain probably approximately correct solutions efficiently.

    Temporal-Logic-Based Semantic Fault Diagnosis With Time-Series Data From Industrial Internet of Things

    Gang Chen, Mei Liu, Zhaodan Kong
    IEEE Transactions on Industrial Electronics, 68(5), 4393-4403
    Journal
    We formalize semantic fault diagnosis for IIoT systems as the task of learning signal temporal logic formulas directly from time-series data. To address combinatorial explosion, we propose an agenda-based, reinforcement-learning-enabled search strategy that discovers scalable and interpretable fault specifications from industrial sensor data.

    Semantic Inference for Cyber-Physical Systems with Signal Temporal Logic

    Gang Chen, Mei Liu, Zhaodan Kong
    2019 IEEE 58th Conference on Decision and Control (CDC)
    Conference
    We present the problem of semantic inference for cyber-physical systems, aiming to translate system behaviors into signal temporal logic specifications automatically. To address combinatorial explosion in formula search, we propose an agenda-based computation framework and formulate semantic inference as a Markov decision process solved with reinforcement learning.
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    How this module fits the SRIS portfolio

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    • Time-series models
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    • Interpretable learning
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