TAN RUNJIAARMINE · ROBOTICS & EMBODIED AI

ROBOT LEARNING · MULTIMODAL INTERACTION · MANIPULATION

Exploring Intelligence.
Building with Robots.

I'm Tan Runjia, also known as Armine, a PhD candidate at NTU working to bring robot learning from industry into everyday life. I am supervised by Lyu Chen.

Third-year PhD · Nanyang Technological University · Singapore

EXPLORE MY WORK ⟶
谭润家 TAN RUNJIA
Robotics &
Embodied AI
CURRICULUM VITAEPDF ↗

RESEARCH DIRECTIONS

Robotic Learning from Human

Reusable robot skills from human instructions, demonstrations and interaction history.

Multimodal LLMs

Aligning language, gestures and visual perception to understand human intent.

Robotic Manipulation

Connecting perception, planning and control for precise, practical robot systems.

SELECTED RESEARCH & PROJECTS

Speech and co-speech gestures identify an object and its placement location.01

Research

DR-TSLM

Grounding “this” and “here” in speech and gestures, so robots can identify what to move and where it belongs.

CIS-RAM 2026 · First author
Official Intrinsic AI for Industry Challenge workcell showing a robot gripper, orange cables and an electronics board.02Official image · Intrinsic

Industry collaboration

Cable Assembly & Contact-Guided Insertion

Robotic cable insertion through visual registration and force feedback, in collaboration with Desay SV Singapore.

Desay SV Singapore · MatrixX
Microscope, cameras, fiducial markers and micromanipulators in the injection setup.03

Research & engineering

Vision-Servoed Microdevice Injection

A hardware-agnostic vision-servoed system for automated, precise injection of implantable microdevices.

ICRA 2026 · Co-first author
ALL PROJECTS & EARLIER WORK ⟶

RECENT UPDATE

  1. Research

    DR-TSLM

    Grounding “this” and “here” in speech and gestures, so robots can identify what to move and where it belongs.

  2. Industry collaboration

    Cable Assembly & Contact-Guided Insertion

    Robotic cable insertion through visual registration and force feedback, in collaboration with Desay SV Singapore.

  3. Research & engineering

    Vision-Servoed Microdevice Injection

    A hardware-agnostic vision-servoed system for automated, precise injection of implantable microdevices.

EXPLORE THE FULL TIMELINE ⟶

INDUSTRY PARTNERS

SELECTED PUBLICATIONS

CIS-RAM 2026

DR-TSLM: Deictic Reference Resolution Via Temporal-Spatial Large Language Model for Human-Robot Interaction

Runjia Tan, Shanhe Lou, Lan Yu, Xuesong Tian, Chen Lv

Conference record ↗Project ⟶

ICRA 2026

An Autonomous and Hardware-Agnostic Vision-Servoed System for Microdevice Injection

Yumin Zheng†, Runjia Tan†, Rui Jiao, Sunwoo Lee

Conference record ↗Project ⟶

Journal of Manufacturing Systems · 82, 1213–1226 · 2025

Large language model-enabled cognitive agent for self-aware manufacturing

Shanhe Lou, Runjia Tan, Yanxin Zhou, Ziyue Zhao, Yiran Zhang, Chen Lv

Paper ↗

CIS-RAM 2024 · 14–19

Best Paper

Multi-modal LLM-enabled Long-horizon Skill Learning for Robotic Manipulation

Runjia Tan, Shanhe Lou, Yanxin Zhou, Chen Lv

Paper ↗Project ⟶

IEEE Robotics and Automation Letters · 7(4), 12451–12458 · 2022

Template-Based Category-Agnostic Instance Detection for Robotic Manipulation

Zhongxu Hu, Runjia Tan, Yanxin Zhou, Junyang Woon, Chen Lv

Paper ↗

† Equal contribution.

VIEW PUBLICATIONS ⟶

LET'S CONNECT

I welcome conversations about robot learning, multimodal interaction, and bringing research into practical robotic systems.

Nanyang Technological University · Singapore

runjia.tan@ntu.edu.sg
GitHub About me