The paper introduces a feedback world model that updates predictions online using the robot’s observed next state after each action, thereby closing the loop between prediction and observation. This lightweight feedback state corrects future predictions without extra training or parameter updates, and the authors provide convergence guarantees under bounded dynamics residuals. Experiments on LIBERO‑Plus, Robomimic, and real‑world tasks show significant reductions in prediction error (up to 76.4%) and improved policy performance, even when applied to pretrained foundation models like Fast‑WAM‑Joint.
By Tuo An, Jindou Jia, Gen Li, Jingliang Li, Chuhao Zhou, Pengfei Liu, Bofan Lyu, Jiaqi Bai, Xinying Guo, Geng Li, Jianfei Yang
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
arXiv:2608.22067v1 Announce Type: cross
Abstract: World-Action Models (WAMs) build robot control on video-generation backbones, which jointly predict dense future visual trajectories and robot action...
By Fenghao Lei, Zhixiong Huang, Long Yang, Jiabao Chen, Jie Cheng, Peilin Huang, Han Fu, Zhuo Li, Xiaoxue Ren
arXiv:2607. 11270v1 Announce Type: cross Abstract: Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities.
By Peijun Tang, Shangjin Xie, Baifu Huang, Binyan Sun, Haotian Yang, Kuncheng Luo, Weiqi Jin, Shilin Fang, Jianan Wang
arXiv:2610.34663v1 Announce Type: cross
Abstract: Video world models trained on large-scale robot data promise a safe and scalable approach for learning and evaluating policies that act in the physic...
By Mattia Scardecchia, Sherry Yang
The paper introduces a Latent World Model (LWM) for robot navigation that predicts action‑conditioned latent feature compatibility instead of reconstructing future observations. By exploiting the correlation between spatial proximity and latent feature similarity, the model evaluates action consequences directly in latent space and supports counterfactual training using sampled action sequences. The learned world model can supervise policy learning from unlabeled video and further improve policies via reinforcement learning entirely within the model, eliminating the need for action annotations and additional environment interaction.
By Zengmao Wang, Wei Gao, Shuhan Shen
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
arXiv:2606. 15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene.
By Jialei Chen, Kai Wang, Kang Chen, Shuaihang Chen, Feng Gao, Wenhao Tang, Zhiyuan Li, Weilin Liu, Zhuyu Yao, Boxun Li, Yuanbo Xu, Chao Yu
arXiv:2609.09210v1 Announce Type: cross
Abstract: Teleoperated demonstrations are often multimodal even when the underlying dynamics are nearly deterministic given the executed action. We argue that...
By Jinting Hang, Zhenhui Cai
DELE-w0.5 is a robotic manipulation framework that predicts future latent states instead of generating full video sequences, thereby inferring robot actions directly from these compact representations. By focusing on physical state changes rather than visual transitions, it reduces model complexity and inference latency. In 480 real‑robot trials across four long‑horizon tasks, DELE‑w0.5 achieved 62.5 % overall task success and 81.3 % macro ordered‑stage progress, outperforming the strongest baseline by 47.5 and 30.7 percentage points.
By Fenghao Lei, Zhixiong Huang, Long Yang, Jiabao Chen, Peilin Huang, Han Fu, Zhuo Li, Xiaoxue Ren
arXiv:2610.16443v1 Announce Type: new
Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control r...
By Yuanpeng He, Ceyao Zhang, Fangjing Li, Kefei Zhu, Lijian Li, Yueheng Li, Tianjia He, Wenpin Jiao, Zhi Jin, Yaodong Yang, Yuanpei Chen
The paper presents an end‑to‑end JEPA world model that enhances latent prediction with inverse dynamics and state alignment to improve goal‑conditioned robotic planning. By preventing latent collapse and grounding representations in physical configuration, the model achieves top success rates on tasks such as TwoRoom, PushT, and OGBench‑Cube, outperforming the baseline LeWorldModel. Ablation studies confirm that state alignment consistently boosts planning success over inverse dynamics alone across all four benchmark tasks.
By Muyuan Liu (GENISOM AI, Beijing, China), Yue Huang (GENISOM AI, Beijing, China), Zheng Liang (GENISOM AI, Beijing, China), Xiang Gao (GENISOM AI, Beijing, China)