Dr. Chelsea Finn
Stanford University
Assistant Professor of Computer Science and Electrical Engineering at Stanford University and Director of the IRIS Lab. Creator of Model-Agnostic Meta-Learning (MAML) and key leader in the Open X-Embodiment robotics foundation initiative.
Areas of focus
Professional niches
Proof of Work
Open X-Embodiment: Robot Learning Datasets and Generalist Policies (RT-X)
Co-led the global Open X-Embodiment consortium uniting 33 academic laboratories across 22 robot types to train RT-X, the world's largest open multi-embodiment robotic policy.
Cross-robot embodiment generalization degrades when sensor modalities or physical gripper geometries differ substantially.
Context: 1M+ robotic trajectory demonstrations across Franka, UR5, Aloha, and mobile manipulators.
View missionModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML)
Authored the landmark ICML 2017 paper creating MAML, a gradient-based meta-learning algorithm that optimizes neural network initializations so that models can learn new tasks from only a few gradient steps.
Second-order Hessian computations impose high memory overhead during meta-training iterations.
Context: Second-order meta-gradient optimization across few-shot classification and robotic continuous control.
View mission