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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…
Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors
Fan Pu, Keshu Wu, Jiwan Jiang +5
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorr…
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…
NavTrust: Benchmarking Trustworthiness for Embodied Navigation
Huaide Jiang, Yash Chaudhary, Yuping Wang +8
There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigatio…
RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks
Mingxuan Yan, Yuping Wang, Zechun Liu +1
To tackle long-horizon tasks, recent hierarchical vision-language-action (VLAs) frameworks employ vision-language model (VLM)-based planners to decompose complex manipulation tasks…