Researcher, Google
2 papers at NeurIPS 2025
Robo2VLM is a framework that generates VQA datasets for robotic manipulation using real-world robot trajectories and non-visual sensor data
We present a reward‑engineering‑free, online Self‑Improvement procedure that enables robotic foundation models to sample-efficiently improve their policies, and autonomously practice and acquire skills generalizing far beyond their imitation data.