Dr. Chelsea Finn
Stanford University
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Chelsea Finn. Every entry undergoes editorial source verification.
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.
Model-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.