Shuran Song

A unified framework for understanding manipulation robustness could accelerate the development of robots that match human dexterity in uncertain environments.

Adapting pretrained policies with just a modest multisensory dataset can enhance robot manipulation performance across diverse tasks without sacrificing prior knowledge.

Robots can now learn new tasks on-the-fly from just one demonstration, revolutionizing how we teach machines to manipulate their environments.

This vine robot can autonomously navigate and manipulate in complex environments, overcoming traditional control limitations with a robust vision-based approach.

DF-ExpEnse boosts sample efficiency in robotic fine-tuning by intelligently balancing exploration and quality, outperforming traditional methods across diverse tasks.

Action-view augmentation can transform how robots adapt to unforeseen obstacles, boosting manipulation success rates significantly.

Simply feeding more history to visuomotor policies hurts performance; GMP solves this by learning when and what to remember, boosting success rates by 30% on memory-intensive robotic tasks.

Runners stick to their pace 60% better and enjoy the workout more when coached by a robot dog than when using an Apple Watch.

Scaling robot learning with human data isn't a simple "more is better" equation; alignment with robot learning objectives is key.

Turn your robot's clumsy pre-trained behaviors into expert-level skills with DICE-RL, a surprisingly stable and efficient RL fine-tuning method.