EmbodiedSkills is a skill-based agent framework that enhances frozen vision‑language‑action (VLA) policies with world action models for consequence‑aware action selection. It defines reusable skills for observation, context encoding, candidate generation, consequence prediction, outcome evaluation, candidate comparison, and action execution, organized into five stages that support both continuous action chunks and pick‑and‑place primitives. The framework improves success rates on MetaWorld MT50, CLIPort, and CALVIN benchmarks by incorporating outcome predictions and lightweight comparators while keeping the core policy and observation encoders frozen.
By Wei Wang, Wenqiao Zhang, Yutong Lin, Yuqian Yuan, Tianwei Lin, Jinhao Mao, Zhenxuan Fan, Mingjian Gao, Yang Dai, Wentong Li, Zheqi Lv, Zheng Dong, Yingjie Niu, Jiaqi Zhu, Jun Xiao, Chao Li, Yueting Zhuang
arXiv:2606. 19297v1 Announce Type: new Abstract: Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation.
By Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin, Nikita Kurlaev, Daria Pugacheva, Albina Burlova, Mikhail Kolosov, Denis Shepelev, Andrey Kuznetsov, Elena Tutubalina, Aleksandr I. Panov, Alexey K. Kovalev, Vlad Shakhuro
arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.
By Jianke Zhang, Xiaoyu Chen, Qiuyue Wang, Mingsheng Li, Yanjiang Guo, Yucheng Hu, Jiajun Zhang, Shuai Bai, Junyang Lin, Jianyu Chen
LoopVLA introduces a recurrent Vision‑Language‑Action architecture that learns to refine multimodal representations, predict actions, and estimate when further refinement is unnecessary. By iteratively applying a shared Transformer block and producing a sufficiency score at each step, it decouples refinement from fixed layer indices and aligns confidence scores with action quality through a self‑supervised objective. Experiments on LIBERO, LIBERO‑Plus, and VLA‑Arena demonstrate that LoopVLA reduces model parameters by 45% and boosts inference throughput up to 1.7× while matching or surpassing strong baselines in task success.
By Boyang Shen, Kaixiang Yang, Hao Wang, Qiuyu Yu, Qiang Xie, Qiang Li, Zhiwei Wang
arXiv:2609.38616v1 Announce Type: cross
Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...
By Yanyan Zhang, Disheng Liu, Xinpeng Li, Chaoda Song, Mohsen Hariri, Debargha Ganguly, Wang Yang, Kai Ye, Bryce Grant, Vipin Chaudhary, Yu Yin
Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge with poor generalization of low-level control.
arXiv:2609.39794v1 Announce Type: new
Abstract: Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, yet adapting them to new tasks and domains rema...
By Zaijing Li, Rui Shao, Bing Hu, Haoyu Zhang, Dongmei Jiang, Liqiang Nie
arXiv:2605.13119v2 Announce Type: replace-cross
Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burde...
By Zixing Lei, Changxing Liu, Yichen Xiong, Minhao Xiong, Yuanzhuo Ding, Zhipeng Zhang, Weixin Li, Siheng Chen
EmbodiedMidtrain bridges the gap between Vision‑Language Models (VLMs) and Vision‑Language‑Action Models (VLAs) by first analyzing the distribution mismatch between their data. It introduces a lightweight proximity estimator that selects VLA‑aligned samples from a large VLM pool, creating the EmbodiedMidtrain‑1M dataset used to mid‑train VLMs before VLA fine‑tuning. Experiments on simulated and real‑world robot tasks show consistent performance gains across different VLM backbones and VLA action heads, with early training steps already benefiting from the improved initialization.
By Yiyang Du, Zhanqiu Guo, Haochen Zhang, Annabella Macaluso, Xin Ye, Liu Ren, Yonatan Bisk, Chenyan Xiong
arXiv:2610.08133v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token seq...
By Owen Du, Yang Yue, Jie Zhang, Jiaqi Pi, Chi Bene Chen, Gao Huang
arXiv:2608. 19589v1 Announce Type: cross Abstract: Pretrained Vision-Language-Action models provide a strong foundation for robot learning, but sequentially adapting them to diverse skills can perturb the representations and velocity mappings used by previous skills, leading to catastrophic forgetting.
By Jiaqi Wang, Zhou Fang, Qiongfeng Shi, Yi Zhou
The paper "ActionCodec: What Makes for Good Action Tokenizers" addresses the lack of design guidance for action tokenization in Vision‑Language‑Action (VLA) models. By deriving information‑theoretic principles—maximizing temporal token overlap, reducing vocabulary redundancy, boosting multimodal mutual information, and ensuring token independence—the authors propose ActionCodec, a tokenizer that improves training efficiency and VLA performance. Experiments show that a SmolVLM2‑2.2B fine‑tuned with ActionCodec reaches a 95.5 % success rate on LIBERO, rising to 97.4 % with architectural tweaks, setting a new state‑of‑the‑art for VLA models without robotics pre‑training.
By Zibin Dong, Yicheng Liu, Shiduo Zhang, Baijun Ye, Yifu Yuan, Fei Ni, Jingjing Gong, Xipeng Qiu, Hang Zhao, Yinchuan Li, Jianye Hao