About
I'm Junkai Huang, a junior at Southwest Jiaotong University (211) studying Electronic Information Engineering — GPA 3.73/4.0 (91.65, ranked 18/81). President of the university robotics association, and embedded lead for the RoboMaster competition team.
My work sits at the intersection of real-time embedded systems and embodied AI. On the hardware side, I build firmware for STM32 microcontrollers, design PCBs, and develop motor control systems. On the AI side, I train and deploy vision-language-action models on real robot hardware — from data collection to sim-to-real transfer to edge deployment on RK3588.
I've published an EI conference paper as first author, co-authored a CCF-B paper, filed an invention patent, and won 9 national-level competition awards including a RoboMaster National 1st Prize.
ISTJ-T — systematic, detail-oriented, and allergic to half-finished work.
What I'm Working On
- ▸Tactile-augmented diffusion policies for contact-rich robotic assembly
- ▸Hierarchical tactile force-control execution framework (slow policy + fast tactile residual)
- ▸Continued development of the RoboMaster robot platform
- ▸Sim-to-real transfer and TeleOp data collection pipelines
Education
Key courses: Engineering Mathematics (94), Circuit Analysis & Design (96), Electronic Devices (96), Power Electronics, Embedded Systems.
Experience & Research
- ▸Built a mobile manipulation system based on Diffusion Policy and VLA (π0.5) for household tasks (wiping tables, transporting objects).
- ▸Jetson edge node + 1080p fisheye wrist camera + RealSense depth camera → wireless inference on RTX 2080Ti server → Rokae ER3 Pro 7-DoF arm + Puwei P500 differential base.
- ▸TeleOp data collection using smartphone AR pose tracking — single phone for full system teleoperation.
- ▸Exploring tactile-augmented diffusion policies for contact-rich assembly tasks.
- ▸Built an embodied AI system combining natural language understanding and robot control using Dobot CR5 arm + AG-95 gripper + dual D435 depth cameras.
- ▸Collected 100+ demonstrations, built LeRobot-format dataset, LoRA fine-tuned π0 VLA model.
- ▸Voice-to-text + camera input → ΔJoint output (6-axis + gripper). Deployed via TCP after Isaac Sim validation.
- ▸"Transformer Pruning and Optimization for Embodied AI: An Embedded Implementation on RK3588"
- ▸Proposed HALSP (Hybrid Adaptive Layer Selection Pruning) for hardware-friendly Transformer sparsification.
- ▸Achieved <100ms response time on RK3588 while maintaining >80% accuracy in a VCM + CNN + LLM multimodal pipeline.
- ▸"A method, system and device for video anomaly object insertion and automatic mask generation"
- ▸Keyframe annotation + trajectory interpolation + VGG19/AdaIN style transfer + Poisson blending. One annotated frame generates 30 interpolated frames with pixel-level masks.
- ▸Infantry (swerve drive): APP/Module/BSP layered C++ firmware on STM32H7. Self-developed MA600 hollow-shaft encoder with field compensation (<0.1° error). Gyroscope-closed-loop gimbal with MIT control — <100ms step response to 0.1 rad, near-zero steady-state error.
- ▸Engineer (7-DoF arm + mecanum): ROS2 + MoveIt2 + IKFast (4ms/solve) + KDL on upper computer. USB CDC with DMA ping-pong buffers + FreeRTOS CRC task. Self-built teach pendant using absolute encoders.
- ▸Results: National 1st Prize (3rd nationally, 1st Southwest) · Alliance Match 3rd Place · Engineer National 2nd Prize — 6 national awards total.
- ▸"Anti-Forgetting Test-time Adaptation for Robust Medical Image Analysis under Distribution Shift"
Awards
Tech Stack
Embedded
Robotics
AI / ML
Hardware
Software
Why Embedded Systems & Embodied AI
I like building things that exist in the physical world. Writing code that makes a motor spin precisely, a robot arm reach for an object, or a gimbal hold steady while the chassis underneath slides sideways at full speed — that feedback loop between software and physics is what keeps me engaged. Embedded systems are where every microsecond matters and every byte counts.
Embodied AI is the natural extension: giving machines the ability to perceive, reason, and act in unstructured environments using learned policies rather than hand-coded rules. I want to work at the intersection of real-time control, hardware design, and machine learning — and I'm building toward that one project at a time.