Learning Native Continuation for Action Chunking Flow Policies
Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories that are not intrinsically smooth. We propose Legato, a training-time continuation method for action-chunked flow-based VLA policies. Specifically, Legato initializes denoising from a schedule-shaped mixture of known actions and noise, exposing the model to partial action information. Moreover, Legato reshapes the learned flow dynamics to ensure that the denoising process remains consistent between training and inference under per-step guidance. Legato further uses randomized schedule condition during training to support varying inference delays and achieve controllable smoothness. Empirically, Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time. Extensive real-world experiments show that Legato consistently outperforms RTC across five manipulation tasks, achieving approximately 10% improvements in both trajectory smoothness and task completion time.
69954df08d17d1ee8c10ecc9
Yufeng Liu
claimed_verified
2026-02-18T09:08:01.932Z
65d72c946a36b5b354f80cf8
/avatars/2cfc99ccd4f52f4a60c7fa40fe4313b7.svg
lyfeng
lyfeng001
user
lyfeng001
69954df08d17d1ee8c10ecca
Hang Yu
69954df08d17d1ee8c10eccb
Juntu Zhao
69954df08d17d1ee8c10eccc
Bocheng Li
69954df08d17d1ee8c10eccd
Di Zhang
69954df08d17d1ee8c10ecce
Mingzhu Li
69954df08d17d1ee8c10eccf
Wenxuan Wu
admin_assigned
2026-02-18T13:43:59.472Z
65192da566e78720a7611ab8
/avatars/97a68200d1bf49e199a741c0a28bab85.svg
wenxuan wu
psdem
user
psdem
69954df08d17d1ee8c10ecd0
Yingdong Hu
admin_assigned
2026-02-18T13:43:50.818Z
64c1d107c8a50776282a6524
/avatars/b6382dbaff0b2fc98e84944f57cad045.svg
Yingdong Hu
Yingdong-Hu
user
Yingdong-Hu
69954df08d17d1ee8c10ecd1
Junyuan Xie
admin_assigned
2026-02-18T13:43:43.652Z
67cfea72ad91643b5cb92b26
/avatars/11937ee700bf6a743cb627928bebd214.svg
Junyuan Xie
piiswrong
user
piiswrong
69954df08d17d1ee8c10ecd2
Junliang Guo
admin_assigned
2026-02-18T13:43:36.712Z
66868659ccb9539da85c4e14
/avatars/515a49363872c23d57a6f75063606348.svg
Junliang Guo
leo-guo
user
leo-guo
69954df08d17d1ee8c10ecd3
Dequan Wang
admin_assigned
2026-02-18T13:43:30.541Z
61ad24836da53246bd6ac410
https://cdn-avatars.huggingface.co/v1/production/uploads/61ad24836da53246bd6ac410/o-FL-C6B77iB94wyAtTuO.png
Dequan Wang
dqwang
user
dqwang
69954df08d17d1ee8c10ecd4
Yang Gao
Learning Native Continuation for Action Chunking Flow Policies
Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories that are not intrinsically smooth. We propose Legato, a training-time continuation method for action-chunked flow-based VLA policies. Specifically, Legato initializes denoising from a schedule-shaped mixture of known actions and noise, exposing the model to partial action information. Moreover, Legato reshapes the learned flow dynamics to ensure that the denoising process remains consistent between training and inference under per-step guidance. Legato further uses randomized schedule condition during training to support varying inference delays and achieve controllable smoothness. Empirically, Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time. Extensive real-world experiments show that Legato consistently outperforms RTC across five manipulation tasks, achieving approximately 10% improvements in both trajectory smoothness and task completion time.
Legato improves action-chunked Vision Language Action models by using training-time continuation methods that ensure smooth trajectories and reduce multimodal switching during real-time execution.
Vision Language Action models action chunking denoising flow-based policies schedule-shaped mixture randomized schedule condition trajectory smoothness multimodal switching task completion time