I am a PhD candidate at Stanford University, where I work with Marco Pavone in the Autonomous Systems Lab. Previously, I’ve worked with Jeannette Bohg during my Master’s, and Rob Shepherd in undergrad. I am also a current 2024 NASA Space Technology Graduate Research Fellow, and was a 2022 NSF Graduate Research Fellow.
In my research, I focus on highly dynamic control, tight integration of learning and model-based optimization, and operating at the limits of performance and safety, for a wide range of robot hardware platforms and learned models.
3D Gaussian splatting + mesh reconstruction + simulation = physics-informed robot planning
Autonomous manipulation of cables with long-reach robotic arms for construction tasks on the Moon.
Safe, low-latency manipulator teleoperation at the limits of performance, maintaining hundreds of safety constraints at kilohertz control rates.
Semantically-safe fallback strategies to prevent out-of-distribution failures in real-time
Optimization-based manipulation planning methods for ReachBot, a novel multi-limbed space robot.
A massive dataset for robot learning, with over 1M trajectories across 22 embodiments.
A large-scale robot manipulation dataset with an emphasis on household tasks. (If you look closely, you'll find my old apartment)
A soft robotic wing capable of multi-degree-of-freedom shape morphing via an elastomeric lattice with embedded optical sensing.
Dynamic collision avoidance with control barrier functions
Fast robot dynamics on CPU, GPU, and TPU, with automatic differentiation support, in Python
An easy-to-use and high-performance framework for constructing and solving Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs)
Free-floating space robot simulation and control, with an emphasis on deformable cargo manipulation and transport in the ISS
