Part I: Differentiable simulation for scalable robot learning.

First, I present a GPU-parallel differentiable multiphysics simulation and a first-order reinforcement learning algorithm that pairs simulation gradients with entropy regularization, for smoother policy optimization on locomotion and manipulation tasks. Next, I introduce hybrid analytic differentiability, combining implicit differentiation, auto-differentiation, and custom analytic Jacobians to compute gradients through contact without modifying forward dynamics. With it, I develop a production-ready differentiable simulation and show how its gradients support initial value problems, trajectory optimization, and system identification.

Part II: Aligning simulation with the real world across diverse embodiments and tasks.

First, I introduce an algorithm for iterative real-to-sim alignment. Alongside, I present a hybrid neural dynamics model that combines learned dynamics correction with analytical inverse dynamics while retaining the simulator’s contact resolution, to produce physically consistent simulation trajectories. Next, I build flexible real-time robot I/O infrastructure for synchronized data collection and policy deployment across different robots, sensors, and interfaces.

In my proposed work, I will explore how differentiable simulation and differentiable rendering can support real-to-sim reconstruction of simulation environments from multimodal real-world data. In my final project, I will study how neural dynamics can scale real-to-sim-to-real learning to dexterous hands and humanoid robots requiring high-dimensional continuous control.