From the 1 of 15 linked papers with an AI index.
15 papers
What Matters for Latent Actions in Robot Learning
Xizhou Bu, Qingda Hu, Lei Zhou +13
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact s…
CheckVLA: Execution-Time Verification with Action-Conditioned World Model for Long-Horizon Mobile Manipulation
Yushan Liu, Peibo Sun, Xintao Chao +8
The paper introduces CheckVLA, a system that uses a frozen action‑conditioned world model to verify and intervene during long‑horizon mobile manipulation when execution deviates fr…
Anticipate Before Acting: Future-State-Conditioned Vision-Language Navigation
Lingfeng Zhang, Zhanguang Zhang, Liheng Ma +2
End-to-end vision-language navigation (VLN) with causal vision-language models maps instructions and egocentric observations directly to actions, but standard behavior cloning supe…
OneVLA: A Unified Framework for Embodied Tasks
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang +10
Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroun…
MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigation
Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu +7
Vision-and-language navigation (VLN) is a key task in Embodied AI, requiring agents to navigate diverse and unseen environments while following natural language instructions. Tradi…
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
Yushan Liu, Peibo Sun, Shoujie Li +7
World Action Models (WAMs) enhance Vision-Language-Action policies by jointly predicting scene evolution and robot actions, but existing methods usually represent the predicted wor…