7 papers
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…
TensorNEAT: A GPU-accelerated Library for NeuroEvolution of Augmenting Topologies
Lishuang Wang, Mengfei Zhao, Enyu Liu +2
The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with…
Tensorized NeuroEvolution of Augmenting Topologies for GPU Acceleration
Lishuang Wang, Mengfei Zhao, Enyu Liu +2
The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with…