Interactive Differentiable Simulation
I am investigating how differentiable simulators can help us reduce the simulation-reality gap. The end-to-end differentiability of such simulators allows us to efficiently optimize their parameters, which means the physics engine can be incorporated into learning-based architectures. By augmenting the original physics engine with neural networks, even unmodeled effects can be captured by such learning-based simulator (see NeuralSim).
My research interests include autonomous robots, simulators, and motion planning.
Best Student Paper at RSS 2021