5 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…
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…
DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalization
Yikai Tang, Haoran Geng, Jindou Jia +5
Imitation learning has emerged as a crucial approach for acquiring visuomotor skills from demonstrations, where designing effective observation encoders is essential for policy gen…
Sorrel: A simple and flexible framework for multi-agent reinforcement learning
Rebekah A. Gelpí, Yibing Ju, Ethan C. Jackson +4
We introduce Sorrel (https://github.com/social-ai-uoft/sorrel), a simple Python interface for generating and testing new multi-agent reinforcement learning environments. This inter…
Social coordination perpetuates stereotypic expectations and behaviors across generations in deep multi-agent reinforcement learning
Rebekah A. Gelpí, Yikai Tang, Ethan C. Jackson +1
Despite often being perceived as morally objectionable, stereotypes are a common feature of social groups, a phenomenon that has often been attributed to biased motivations or limi…