4 papers
VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
Yixiao Wang, Mingxiao Huo, Zhixuan Liang +8
Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generali…
Continual Learning with Global Alignment
Xueying Bai, Jinghuan Shang, Yifan Sun +1
Continual learning aims to sequentially learn new tasks without forgetting previous tasks' knowledge (catastrophic forgetting). One factor that can cause forgetting is the interfer…
LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
Xiang Li, Cristina Mata, Jongwoo Park +8
Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for…
Theia: Distilling Diverse Vision Foundation Models for Robot Learning
Jinghuan Shang, Karl Schmeckpeper, Brandon B. May +4
Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification…