Zechu Li

One prompt 💬. One simulator. A trained robot policy 🤖.

🚢 HARBOR autonomously builds the task, designs rewards, trains, tunes, and evaluates — end to end. Now accepted at #CoRL2026.

From prompt to policy, fully autonomous, 1.5h. 👇

I’ve always been fascinated by whether robots can keep improving themselves in the real world.

We introduce 🔎 FIND: a step toward agentic, reset-free self-improvement—deciding what to do next, evaluating outcomes, and learning continuously. #AgentAI #Robotics #VLA #EmbodiedAI

Unlike humans who favor a dominant hand for fine dexterous skills, robots should execute ambidextrous manipulation with equal proficiency.
Introducing SYMDEX, our algorithm for Ambidextrous Bimanual Manipulation leveraging robot’s inherent bilateral symmetry as an inductive bias.

Our method significantly outperforms other diffusion-based methods on high-dimensional control benchmarks, and is also competitive with state-of-the-art non-diffusion based RL methods while requiring fewer algorithmic design choices and smaller update-to-data ratios!

How can an RL agent successfully solve a task while showcasing versatility of behaviors—a property intuitive to intelligent systems like humans?

Introducing Deep Diffusion Policy Gradient (DDiffPG) - our new algorithm for learning multimodal behaviors from scratch! (1/5)