Tag
This paper proposes Intention Distillation (INDI) to distill behavior intent into the action decoder of Vision-Language-Action models, improving performance on benchmarks like SimplerEnv-Bridge and real-world tasks.
This paper introduces Complementary Action Modeling (CAM), a task that identifies or generates procedural counterparts of automotive maintenance instructions by modifying the action phrase while preserving context. Using a German automotive dataset, the authors examine candidate matching and controlled Seq2Seq generation to model these complementary instructions.
RepWAM introduces a world action modeling approach using representation visual-action tokenizers, aiming to learn unified visual and action representations for planning and control.