WENQIAN (VINCENT)
ZHANG
Robotics & Embodied AI Researcher
I build and study learning systems for real-world robot manipulation—from hardware integration and teleoperation to demonstration data, imitation learning, and VLA evaluation.
Computer Engineering @ UC Riverside · Robotics Intern in San Francisco
Research & Focus
How can embodied agents infer what matters and act robustly beyond memorized trajectories?
I work across two connected areas: real-world robot learning and multimodal behavior understanding.
My robotics work spans the full stack—building manipulation platforms, integrating cameras and teleoperation systems, collecting demonstrations, training imitation-learning policies, and evaluating vision-language-action models on physical hardware.
At UC Riverside, I built a Franka FR3 robot-learning cell with GELLO teleoperation and evaluated generalist policies under real-world distribution shifts. At an early-stage robotics startup, I am building dual-arm manipulation, data-collection, and autonomous rollout pipelines.
I am also the first author of a paper accepted to the CVPR 2026 CV4Animals Workshop on context-aware multimodal intent inference.
Selected Works
Publication & Recognition
CVPR 2026 CV4Animals Workshop
Context as Prior: Bayesian-Inspired Intent Inference for Non-Speaking Agents with a Household Cat Testbed
First-author accepted poster paper on intent inference for non-speaking agents, using household cats as a real-world testbed for context-aware multimodal reasoning.
May 2026 · UC Riverside
Chancellor's Research Fellowship
Undergraduate research fellowship supporting the extension of CatSignal into unsupervised multimodal behavior discovery.
Apr 2026 · UC Riverside
Undergraduate Education Mini-Grant
Travel support to present the accepted paper at the CVPR 2026 CV4Animals Workshop.
Let's Connect
Open to research collaborations and strong opportunities in robotics and embodied AI.
Beyond research: games, photography, travel, quiet time with literature—and Yuanbao, my British Shorthair, who unexpectedly inspired a paper.
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