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:2608.30378v1 Announce Type: cross
Abstract: Direct vision-language-action policies generate continuous robot actions efficiently, but standard behavior cloning leaves two complementary gaps: th...
By Botong Zhao, Fang Yu, Tim, Senhua Zhu, Xinyuan Chen, Yue Lu
arXiv:2610.05166v2 Announce Type: replace
Abstract: A safe action is not necessarily a viable one. A frozen vision-language-action (VLA) policy can favor a locally admissible move that leaves no poli...
By Tu Nguyen, Matthieu Zimmer, Vu Anh Vu, Ziyi Wang, Jannik Hammel Nielsen, Xuebing Zhou, Haitham Bou Ammar
arXiv:2609.39235v1 Announce Type: cross
Abstract: World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet exis...
By Ali Alrasheed, Basim Azam, Naveed Akhtar
ForeAct3D is a framework that grounds future world modeling in Vision‑Language‑Action policies by decoding depth, semantic segmentation, and camera pose from policy representations into current and future 3D scene states. The future queries are conditioned on the policy‑generated action chunk, and a physical‑consistency closure enforces background staticity, instance‑level rigidity, and wrist‑camera pose alignment with end‑effector kinematics. This approach eliminates the need for future prediction at inference and achieves high success rates on LIBERO and CALVIN, outperforming the base policy across all suites.
By Zhe Tao, Feiran Wang, Gaowen Liu, Ramana Rao Kompella, Yan Yan
arXiv:2606. 01095v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies and World-Action Models (WAM) represent two increasingly important paradigms for robotic manipulation.
By Hung Mai, Bin Zhu, Tuan Do
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: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
The survey "World-Action Models for Robot Learning and Control" reviews recent advances in coupling future world prediction with executable action generation for robots in open environments. It clarifies the scope of World-Action Models (WAMs) relative to conventional world models, model-based RL, and Vision‑Language‑Action policies, and organizes existing methods through a unified taxonomy covering representations, transition modeling, action interfaces, architectures, training pipelines, data modalities, and scaling strategies. The paper also surveys applications in manipulation, navigation, and autonomous driving, summarizes datasets, benchmarks, and metrics, and discusses key challenges such as action alignment, spatial consistency, long‑horizon memory, and efficient inference.
By Zuxing Lu, Hongjia Zhai, Guanzhi Wang, Huajian Zeng, Jiaqi Yang, Jingyu Liu, Lei Cheng, Yuantai Zhang, Yuheng Qiu, Zezhou Cheng, Ivan Laptev, Danfei Xu, Benjamin Riviere, Giuseppe Loianno, Eric Xing, Xingxing Zuo
The paper surveys 160 benchmarks from 2017‑2026 that evaluate predictive embodied intelligence, categorising them into policy suites, embodied agents, world‑model evaluation, and prediction‑to‑action bridges. It finds that most benchmarks are model‑agnostic, rarely compare Vision‑Language‑Action policies to world models, and seldom turn predictions into executed actions. The authors argue that the lack of benchmarks designed to directly test the closed‑loop advantage of world models prevents the field from answering whether such models truly improve robotic performance.
By Gaytri Jena, Kapil Wanaskar, Vinija Jain, Aman Chadha, Vasu Sharma, Amitava Das
CoDA is a framework that lets a simulation data engine and an action policy evolve together using a shared memory of environment construction, policy failures, and learning progress. The memory stores validated diagnoses from a vision‑language critic, which are turned into generation programs that recreate challenging scenarios and training programs that help the policy improve. Using this approach, CoDA boosts out‑of‑distribution success on several manipulation tasks, raising success rates from 5.3 % to 27.6 % and achieving up to 84 % success on LIBERO‑Pro under perturbations.
By Tianjiao Yu, Zhilin Ou, Yifan Shen, Xinzhuo Li, Yuanzhe Liu, Onkar Kishor Susladkar, Fan Wang, Ismini Lourentzou
arXiv:2609.38057v1 Announce Type: new
Abstract: Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action m...
By Shiyang Zhou, Xionghao Wu, Wenbo Li, Shenghe Zheng, Jiyao Zhang, Songsong Yu, Yijun Yang, Jianhui Liu, Haoze Sun, Senqiao Yang, Li Jiang, Jingyong Su, Haoyang Huang, Zhuotao Tian