arXiv AI
4d ago

ExecVLA: Following Fine-Grained Execution Constraints in Vision-Language-Action Models with Bi-Level Action Representation

The paper introduces ExecVLA, a framework for vision‑language‑action models that separates goal‑oriented behavior from execution‑specific constraints using a bi‑level action representation and supervised reasoning tokens. It adds explicit objectives to keep goal representations stable while preserving execution constraints, and builds datasets with fine‑grained reasoning annotations in simulation and on a Realman‑75 robot. Experiments on LIBERO and the real robot demonstrate better goal completion and notably more reliable adherence to instruction‑specified execution constraints.

By Gaoge Han, Zhengqing Gao, Ziwen Li, Jiaxin Huang, Shaoli Huang, Fakhri Karray, Mingming Gong, Tongliang Liu
arXiv AI
Jul 28

A Few Words Go a Long Way: Language Guided Robot Policy Synthesis

arXiv:2607. 23784v1 Announce Type: cross Abstract: While vision-language-action models have demonstrated impressive zero-shot manipulation capabilities, they remain fundamentally black box policies that are difficult to interpret, adapt, or correct when they inevitably fail.

By Daphne Chen, Archit Ritesh Jain, Eric Goossen, Emma Romig, Michael Murray, Nick Walker, Maya Cakmak