I am an undergraduate student in Automation at Tsinghua University. I work on embodied AI and robot learning, multi-agent coordination and inter-robot communication, reinforcement learning, and diffusion-based generative models.

I am currently a research intern with Prof. Wei-Chiu Ma at Cornell University, asking when two robots need to share what they see — and what they should send. I also work with Prof. Ge Liu at UIUC on the theory of diffusion models that generate continuous and discrete quantities at once.

I will be applying to PhD programs for Fall 2028.

News

  • Oct 2026Our paper with Chenhao Lu on silent faults at the environment–trainer boundary in RL was accepted to the CoRL 2026 Workshop on Everything Beneath the Policy.
  • Sep 2026Resumed PlugRL with Chenhao Lu, now a wire protocol that lets environments running anywhere be trained by RL.
  • Jun 2026Joined Prof. Wei-Chiu Ma's group at Cornell as a research intern, working on shared perception between humanoids.
  • Mar 2026Started working with Prof. Ge Liu at UIUC on noise schedules in joint continuous–discrete diffusion.

Publications

Manuscripts

  • Humanoid2Humanoid: Shared Sensing for Cooperative Loco-Manipulation. Zuo Gou et al., Wei-Chiu Ma. In preparationTarget: CVPR 2027
  • Relative Noise Schedules in Joint Continuous–Discrete Diffusion. Zuo Gou et al. In preparationTarget: ICML 2027

Research Experience

Cornell University Jun 2026 – Nov 2026

Research Intern, Dept. of Computer Science · Advisor: Prof. Wei-Chiu Ma
  • Studying how two humanoids can share what they see in real time, so that a robot walking backward climbs stairs hidden from its own cameras by relying on its partner's view.
  • Established when such sharing matters: robots usually infer hidden terrain from gait and contact, and only in specific conditions does the partner's view become indispensable, recovering 20–40 success points.
  • Built decentralized policies that act on their own sensing plus messages from the partner, halt safely when the link drops and resume when it returns; demonstrated a simulated pair climbing stairs together.

University of Illinois Urbana-Champaign Mar 2026 – Present

Research Intern, Dept. of Computer Science · Advisor: Prof. Ge Liu
  • Studying how a diffusion model that generates continuous and discrete quantities at once — atom positions with atom types and bonds — should pace noise across its two channels, a choice now hand-tuned per dataset.
  • Characterized when the likelihood bound is indifferent to that pacing and what the best schedule looks like when it is not, and confirmed the predictions on public molecular models.
  • Showed that reported likelihoods are dominated by a single coarse step of the sampler, and built an estimator reaching the same precision with roughly 2,600× less computation.

Tsinghua University, IIIS Jul 2025 – Jan 2026

Research Assistant · Advisor: Prof. Huazhe Xu
  • Started PlugRL with Chenhao Lu; the project continues as open source (see below).

Tsinghua University, Dept. of Automation Sep 2024 – Jun 2025

Research Assistant · Advisor: Prof. Yebin Liu
  • Developed a diffusion model that generates physically plausible hand–object interaction for dexterous manipulation, with the simulation environments and demonstration pipelines it was trained on.

Open Source

PlugRL 2025 – Present

  • With Chenhao Lu, co-developed PlugRL, a wire protocol between environments and RL trainers that lets environments on other machines, in other languages, or outside a trainer's cluster still be trained. Wrote the protocol specification and its conformance checkers.
  • Showed that silent faults at the environment–trainer boundary mostly slip past learning-curve checks and clean evaluation, while a step ledger kept on both sides caught and located all 585 faulty runs it checked, for a median 3% of training time on Pendulum (CoRL 2026 workshop paper).
π0.5 acting in LIBERO through PlugRL's wire protocol: the simulator runs in its own process, the policy on the server.

Education

Tsinghua University Sep 2023 – Expected Jun 2028

B.Eng. in Automation (Xinya College & Dept. of Automation)
  • GPA: 3.8/4.0
  • Tsinghua Comprehensive Scholarship (2024, 2025); Luo Yuehua Scholarship (2024)

Technical Skills

Programming Python (PyTorch), C++, CUDA, MATLAB, Shell
Robotics & simulation IsaacGym, Isaac Lab, MuJoCo, Legged Gym, RSL-RL
Infrastructure Linux, Slurm clusters, distributed multi-GPU training, Weights & Biases