Self-Adapting Improvement Loops for Robotic Learning
Video generative models trained on expert demonstrations have been utilized
as performant text-conditioned visual planners for solving robotic tasks.
However, generalization to unseen tasks remains a challenge. Whereas improved
generalization may be facilitated by leveraging learned prior knowledge from
additional pre-collected offline data sources, such as web-scale video
datasets, in the era of experience we aim to design agents that can
continuously improve in an online manner from self-collected behaviors. In this
work we thus propose the Self-Adapting Improvement Loop (SAIL), where an
in-domain video model iteratively updates itself on self-produced trajectories,
collected through adaptation with an internet-scale pretrained video model, and
steadily improves its performance for a specified task of interest. We apply
SAIL to a diverse suite of MetaWorld tasks, as well as two manipulation tasks
on a real robot arm, and find that performance improvements continuously emerge
over multiple iterations for novel tasks initially unseen during original
in-domain video model training. Furthermore, we discover that SAIL is
surprisingly robust regarding if and how the self-collected experience is
filtered, and the quality of the initial in-domain demonstrations. Through
adaptation with summarized internet-scale data, and learning through online
experience, we thus demonstrate a way to iteratively bootstrap a
high-performance video model for solving novel robotic tasks through
self-improvement.
684862d27d59df5350947d20
Calvin Luo
claimed_verified
2025-06-11T08:35:38.255Z
676b5ea796427da73e73c301
/avatars/347251b1005830c4fe2dd6a28664f40d.svg
Calvin Luo
user
calvinyluo
684862d27d59df5350947d21
Zilai Zeng
684862d27d59df5350947d22
Mingxi Jia
684862d27d59df5350947d23
Yilun Du
684862d27d59df5350947d24
Chen Sun
claimed_verified
2025-06-11T08:35:36.330Z
64b6bac29bdb2adec64b1f86
https://cdn-avatars.huggingface.co/v1/production/uploads/64b6bac29bdb2adec64b1f86/516JDGISCKjtYvaYvprp4.jpeg
Chen Sun
user
jesu9
Self-Adapting Improvement Loops for Robotic Learning
Video generative models trained on expert demonstrations have been utilized
as performant text-conditioned visual planners for solving robotic tasks.
However, generalization to unseen tasks remains a challenge. Whereas improved
generalization may be facilitated by leveraging learned prior knowledge from
additional pre-collected offline data sources, such as web-scale video
datasets, in the era of experience we aim to design agents that can
continuously improve in an online manner from self-collected behaviors. In this
work we thus propose the Self-Adapting Improvement Loop (SAIL), where an
in-domain video model iteratively updates itself on self-produced trajectories,
collected through adaptation with an internet-scale pretrained video model, and
steadily improves its performance for a specified task of interest. We apply
SAIL to a diverse suite of MetaWorld tasks, as well as two manipulation tasks
on a real robot arm, and find that performance improvements continuously emerge
over multiple iterations for novel tasks initially unseen during original
in-domain video model training. Furthermore, we discover that SAIL is
surprisingly robust regarding if and how the self-collected experience is
filtered, and the quality of the initial in-domain demonstrations. Through
adaptation with summarized internet-scale data, and learning through online
experience, we thus demonstrate a way to iteratively bootstrap a
high-performance video model for solving novel robotic tasks through
self-improvement.
SAIL, a self-adapting improvement loop, iteratively enhances a video model using self-produced data and pretrained models, achieving continuous performance improvement on novel robotic tasks.
Self-Adapting Improvement Loop SAIL in-domain video model self-produced trajectories internet-scale pretrained video model MetaWorld tasks manipulation tasks real robot arm online experience bootstrapping high-performance video model