5 papers · 1 filter
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
Mingxuan Yan, Litian Gong, Dihong Huang +19
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and…
Flow Matching for Robot Foundation Models
Kuangji Zuo, Tuo An, Xinying Guo +9
Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action models (WAMs) additionally…
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
Ning Zhu, Mengfei Zhao, Yikai Tang +5
Simulation demonstrations can supplement scarce real-world data for robot policy co-training. However, the value of using data curation to actively select these demonstrations for…
AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation
Mengfei Zhao, Dihong Huang, Yikai Tang +12
Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale because they rely on speciali…
APEX: Adaptive Policy Execution for Precise Manipulation
Mengfei Zhao, Chenxi Jiang, Tuo An +2
Modern imitation learning methods, including visuomotor and Vision-Language-Action (VLA) policies, typically output high-level action references that are executed by low-level cont…