BURGER is a benchmark for evaluating vision‑language‑action models on robotic drawing tasks. It provides a scalable, prompt‑driven pipeline that generates continuous drawing trajectories in both simulation and the real world, with 28,200 simulated demonstrations across 282 tasks. The benchmark enables fine‑grained analysis of language grounding, spatial composition, and action generation, revealing distinct failure patterns among five representative VLA models and demonstrating that some models can generalize to unseen compositions and scale with task diversity.
By Xiangyu Xu, Bangshuai Peng, Ziyi Liu, Mengjie Zhao, Chenhao Lu, Zhengyang Fan, Huazhe Xu, Kaizhe Hu
arXiv:2606.18363v3 Announce Type: replace-cross
Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through...
By Haowen Liu, Xirui Li, Shaoxiong Yao, Peng Shi, Tianyi Zhou, Jia-Bin Huang, Furong Huang, Jiayuan Mao
The paper introduces RoboHarness, a simple robotic harness that equips large language models (LLMs) with a visual‑geometric control panel for direct manipulation of embodied tasks. By enabling LLM agents to understand and invoke actions without complex vision‑language‑action (VLA) training, RoboHarness allows models such as Qwen3.8‑Flash‑Next to outperform state‑of‑the‑art VLA models on the BEHAVIOR Challenge 2025 tasks. The authors provide an open‑source implementation of the system.
By Bince Qu, Wei Chen, Bo Zhang
arXiv:2606. 18363v1 Announce Type: cross Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents.
By Haowen Liu, Xirui Li, Shaoxiong Yao, Peng Shi, Tianyi Zhou, Jia-Bin Huang, Furong Huang, Jiayuan Mao
arXiv:2610.14018v1 Announce Type: cross
Abstract: The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical wo...
By Bince Qu, Wei Chen, Bo Zhang
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
By Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu