5 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation
Ruicheng Li, Qixiu Li, Ruichun Ma +8
Vision-language-action (VLA) models have achieved impressive generalization in robotic manipulation, and recent memory-augmented VLAs have relaxed the Markovian assumption by condi…
MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation
Xingyuming Liu, Ruichun Ma, Heyu Guo +7
Humans naturally leverage diverse sensing modalities to interact with the physical world, while most Vision-Language-Action (VLA) models for robotics rely solely on RGB observation…
Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory
Runxi Cheng, Yuchen Guan, Yongxian Wei +7
Scaling conditional memory offers a promising way to increase language-model capacity, but existing methods such as Engram learn large memory tables from scratch during pre-trainin…
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
Zhiyuan Feng, Qixiu Li, Huizhi Liang +12
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large…
TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning
ZhiYuan Feng, Yu Deng, Ruichuan An +11
In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean task specification. Such re…
MobileManiBench: Simplifying Model Verification for Mobile Manipulation
Wenbo Wang, Fangyun Wei, QiXiu Li +5
Vision-language-action models have advanced robotic manipulation but remain constrained by reliance on the large, teleoperation-collected datasets dominated by the static, tabletop…