I am a first-year Master’s student at the Key Laboratory of Autonomous Intelligent Unmanned Systems(AIUS), Harbin Institute of Technology (HIT), China.
My research interests lie in Embodied AI and Robotics, especially learning-based manipulation / dexterous skills and reinforcement learning for robot control. I am also actively exploring vision-language-action (VLA) models and other learning-based control approaches for generalizable manipulation.
I am seeking a visiting research internship throughout 2026(6–12 months). I hope to work under close mentorship, contribute to a strong research direction, and deliver high-quality, publishable results, with the goal of making one top-tier conference submission in 2026.
If you are interested in collaboration or hosting, please feel free to contact me at chentingjia1209@163.com. You can also download my CV here.
🔥 News
- 2026.02: 📨 Submitted ResMerge to ICML 2026 (under review).
- 2025.11: 🔥 Started my research intership at the PKU-PsiBot Joint Lab(Peking).
- 2025.08: 🎉 As an operator and algorithm developer, won a Silver Medal in the material sorting track of the 2025 World Humanoid Robot Games–Competition.
- 2025.07: 🔥 Started my Embodied AI internship at Standard Robots(Shenzhen)
- 2025.03: 🔥 Started my engineering project at Harbin Institute of Technology (Suzhou) Research Institute
🤖 Projects
1) GR00T-N1.6 Fine-tuning on LIBERO — 3B VLA model · PEFT training · 97.8% avg success
Role: End-to-end fine-tuning + evaluation infrastructure · Period: 2026
Role: End-to-end fine-tuning + evaluation infrastructure · Period: 2026
Fine-tuned NVIDIA GR00T-N1.6 (3B VLA) on four LIBERO suites with a parameter-efficient recipe (train projector + diffusion decoder, freeze vision/LLM), and achieved 97.8% average success (782/800) with released, benchmark-validated checkpoints.
- Parameter-efficient training: froze vision encoder + LLM, trained projector + diffusion decoder (~40% trainable params)
- Config-driven parallel evaluation (5 envs) with automatic video recording and CSV summaries
- Released 4 validated fine-tuned checkpoints on HuggingFace (Spatial/Object/Goal/LIBERO-10)
Outcome: 97.8% success (782/800 episodes) across 4 LIBERO suites; +0.8% over the published baseline under the same evaluation protocol.
VLA
Language-conditioned Manipulation
LIBERO
GR00T-N1.6
2) RDT Fine-tuning on LIBERO — Language-conditioned manipulation (Full FT vs LoRA)
Role: Lead implementer (data → training → evaluation) · Period: 2025
Role: Lead implementer (data → training → evaluation) · Period: 2025
Built an end-to-end supervised fine-tuning pipeline for Robotics Diffusion Transformer (RDT) on LIBERO (HDF5 parsing, SigLIP-style vision preprocessing, T5-XXL embedding caching, DeepSpeed training, and automated checkpoint evaluation), and quantified key tradeoffs (Full FT 76–94% vs LoRA ~20%).
- Implemented LIBERO HDF5 → RDT input pipeline (state/action remapping, SigLIP-style image preprocessing, augmentation)
- Added T5-XXL language embedding caching to avoid repeated encoding (~30% training speedup)
- Systematic comparison: Full fine-tuning reaches 76–94% (suite-dependent) while LoRA saturates around ~20%
Outcome: Identified practical bottlenecks and levers: inference dominates evaluation time (~375 ms/step, ~69%), and diffusion steps (H) + action chunk length (N) significantly affect success rate.
Imitation Learning
Diffusion Policy
LIBERO
DeepSpeed
LoRA
3) RDT Integration into RLinf — Diffusion policy as an RL-compatible agent (BC eval ready)
Role: Core implementer (model + env interface + evaluation) · Period: 2026
Role: Core implementer (model + env interface + evaluation) · Period: 2026
Integrated a diffusion-based manipulation policy (RDT) into the RLinf framework by aligning observations (including joint proprioception), implementing DDPM action-chunk sampling and LIBERO action extraction, and enabling checkpoint-based evaluation; PPO-style RL fine-tuning is in progress due to diffusion log-probability requirements.
- Wrapped RDT as an RLinf policy with DDPM-based action generation and action-space extraction for LIBERO execution
- Extended LIBERO env + RLinf I/O to include 9-DoF joint proprioception required by RDT (arm + gripper)
- Identified the key RL blocker: diffusion policies do not directly provide action log-probabilities required by PPO-style algorithms (implementation in progress)
Outcome: Evaluation pipeline works with pretrained/fine-tuned RDT checkpoints on LIBERO; PPO-style RL fine-tuning is under development due to diffusion log-probability challenges.
Reinforcement Learning
Diffusion Policy
RLinf
LIBERO
4) OptiTrack Teleoperation Data Capture — NatNet → PoseStamped/TF → RViz/MoveIt2
Role: System Integration + ROS2/C++ Development · Period: 2025
Role: System Integration + ROS2/C++ Development · Period: 2025
Implemented a motion-capture pose streaming module that converts OptiTrack Motive (NatNet) rigid-body tracking into ROS2 PoseStamped/TF and maps it into RViz/MoveIt2 for teleoperation demonstration capture, logging, and simulation-side verification.
- Bridged OptiTrack Motive (NatNet) rigid-body tracking into ROS2 as PoseStamped / TF for downstream robotics pipelines
- Enabled RViz/MoveIt2-side pose mapping and visualization for rapid iteration and debugging of teleop demonstrations
- Supports dataset collection by recording the pose stream (e.g., rosbag2) as supervised signals for imitation learning
Outcome: Provided a practical MoCap-to-ROS2 interface for teleoperation demonstration capture and simulation-side inspection (RViz/MoveIt2).
Teleoperation
Demonstration Data
ROS2
NatNet
💻 Internships
PKU–PsiBot Joint Lab
Research Intern
· Beijing, China
2025-11 – 2026-02
- Embodied AI research internship.
Standard Robots
Embodied AI Intern
· Shenzhen, China
2025-07 – 2025-09
- Worked on robotics engineering tasks.
📝 Publications
ResMerge
ICML 2026 · Under Review
4th Author
Contributions: Small-model ablation experiments on LIBERO-Object (single RTX 4090), evaluation scripts (result aggregation + plots), and core paper figures (teaser + method diagram).
🎖 Honors & Awards
- 2025.09: 2024-2025 HIT National Special Scholarship
- 2025.08: 2025 World Humanoid Robot Games–Competition Silver Medal
- 2025.03: 2024-2025 HIT Excellent Student Worker
- 2024.08: National First Prize and Ranked Second in National University Students Intelligent Car Race
- 2024.08: National Second Prize and Patent Publication(second inventor) in China University Intelligent Robot Creative Competition
- 2024.07: National College Student Innovation and Entrepreneurship Project
- others: Outstanding student(2022-2023,2023-2024),Second Prize Scholarship(2022-2023,2023-2024,2024-2025)