2017 · Robotics
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextChelsea Finn
Learning adaptable skills from limited experience.
Finn studies how learned systems acquire skills and adapt to new situations. This selection connects meta-learning with robot learning and generalist policies, including work that combines language, perception, and action.
Selected work
10 papers
2023 · Alignment
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Direct Preference Optimization derives a preference-learning objective that avoids training a separate reward model in the studied setup.
Paper & context2023 · Robotics
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
RT-2 transfers knowledge from vision-language models into robotic control by representing actions in a compatible format.
Paper & context2023 · Robotics
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment combines robotic data across embodiments to study generalization and reusable robot policies.
Paper & context2022 · Robotics
RT-1: Robotics Transformer for Real-World Control at Scale
RT-1 uses a robotics transformer trained on diverse real-world demonstrations to map camera images and instructions to robot actions, studying generalization and control at scale.
Paper & context2026 · Robotics
WHALE: A Simple Recipe for Joint Harness-Weight Optimization
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Robotics
Q-Learning With World Models
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Robotics
Pre-training Visual Dexterity in Simulation
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Robotics
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Robotics
Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
Selection & sources