Bio

I currently lead the whole-body manipulation team at Robotics and AI Institute (RAI Institute). Our research combines reinforcement learning, imitation learning, and large-scale simulation to enhance robot capabilities in manipulation and locomotion. Our goal is to bridge theoretical foundations with practical algorithmic solutions that enable robots to achieve human-level whole-body manipulation in real-world environments.

During my PhD at Stanford and as intern at Google Robotics, I built a differentiable physics engine from scratch, enabling gradients to propagate through contact dynamics for optimization and learning. My research focused on developing fast optimization algorithms for simulation, planning, and control of robotic systems. I applied differentiable physics to trajectory optimization, planning, and reinforcement learning for locomotion and manipulation. I developed optimization algorithms that enable game-theoretic reasoning for autonomous vehicles.

Highlight of the team

Combining sampling and learning for dynamic whole-body manipulation

In our latest project, we combined reinforcement learning with sampling-based optimization to enable legged robots to dynamically manipulate large, heavy objects with coordinated use of their arms, legs, and body. We adopted a hierarchical architecture in which a learned locomotion policy handles balance and movement at the low level, while a high-level controller reasons about the task in a reduced space of base velocities and end-effector positions. Sampling-based control discovers forceful, multi-contact strategies by simulating many futures in parallel, allowing the robot to perform dynamic loco-manipulation at human cadence.

Learning from planner demonstrations

With Jacta, we combined reinforcement learning with sampling-based algorithms to solve contact-rich manipulation tasks. While sampling-based planners can quickly find successful trajectories for complex manipulation tasks, the solutions often lack robustness. We leveraged a reinforcement learning algorithm to enhance the robustness of a set of planner demonstrations, distilling them into a single policy.