Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Vision-language-action (VLA) models extend vision-language models (VLM) by
integrating action generation modules for robotic manipulation. Leveraging
strengths of VLM in vision perception and instruction understanding, VLA models
exhibit promising generalization across diverse manipulation tasks. However,
applications demanding high precision and accuracy reveal performance gaps
without further adaptation. Evidence from multiple domains highlights the
critical role of post-training to align foundational models with downstream
applications, spurring extensive research on post-training VLA models. VLA
model post-training aims to address the challenge of improving an embodiment's
ability to interact with the environment for the given tasks, analogous to the
process of humans motor skills acquisition. Accordingly, this paper reviews
post-training strategies for VLA models through the lens of human motor
learning, focusing on three dimensions: environments, embodiments, and tasks. A
structured taxonomy is introduced aligned with human learning mechanisms: (1)
enhancing environmental perception, (2) improving embodiment awareness, (3)
deepening task comprehension, and (4) multi-component integration. Finally, key
challenges and trends in post-training VLA models are identified, establishing
a conceptual framework to guide future research. This work delivers both a
comprehensive overview of current VLA model post-training methods from a human
motor learning perspective and practical insights for VLA model development.
(Project website: https://github.com/AoqunJin/Awesome-VLA-Post-Training)
