Jiarong Li
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    • CRPE-MViT: An enhanced transformer model with circular relative positional encoding for the condition identification of zinc oxide rotary kiln
    • Enhancing cross-domain data analytics through multi-source transfer learning
    • Demo: SolarSense: A self-powered ubiquitous gesture recognition system for industrial human-computer interaction
    • Gesture recognition and control based on visible light communication
    • Indoor health monitoring with VLC-based passive posture monitoring
    • Integrated sensing, lighting and communication based on visible light communication: A review
    • LiFall: Passive indoor fall detection system based on illumination and visible light communication networks
    • PhD forum abstract: Ubiquitous sensing system for activity and gesture recognition via optical and energy-harvesting technologies
    • Poster: Real-time material and texture recognition using visible light communication
    • PowerGest: Self-powered gesture recognition for command input and robotic manipulation
    • VLocSense: Integrated VLC system for indoor passive localization and human sensing
    • A triboelectric gait sensor system for human activity recognition and user identification
    • SolareSkin: Self-powered visible light sensing through a solar cell e-skin
    • Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications
    • TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification
    • Triboelectric nanogenerators enabled internet of things: A survey
    • Finite-time distributed event-triggered consensus control for leader-following general linear multi-agent systems
    • Observer-based decentralized event-triggered consensus for leader-following linear multi-agent systems
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  • Projects
    • Baxter Catch Ball
    • Sokoban playing with Baxter

Baxter Catch Ball

May 1, 2018 · 1 min read
Go to Project Site

This project develops a Baxter robot system that uses stereo vision and both physical and machine learning models to track and catch a thrown ball, comparing prediction methods to improve precision in dynamic motion planning.

Last updated on May 1, 2018
Robotics
Jiarong Li
Authors
Jiarong Li
PhD Candidate

Sokoban playing with Baxter Dec 1, 2017 →

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