Box-WAM introduces a new approach to world action models (WAMs) by replacing dense future video predictions with oriented bounding box trajectories of task-relevant objects. This compact, object-centric representation reduces computational cost during training and inference while improving action prediction performance. The method demonstrates that box trajectory prediction can serve as an efficient alternative to full-frame video-based world action modeling for robotic manipulation tasks.
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
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
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
Video Prediction Policy 2 (VPP2) is a new world action model that improves zero‑shot generalization for both video prediction and action generation. It achieves this by pretraining a large, diverse manipulation video dataset with event‑level supervision, then distilling the model into a single‑step visual planner and adding a mixture‑of‑transformers action module. Experiments show VPP2 outperforms leading baselines on open‑ended video prediction, real‑world zero‑shot manipulation, and several challenging benchmarks.
By Yanjiang Guo, Haodong Yan, Zhide Zhong, Zhongru Zhang, Qingyuan Yang, Qingzhou Lu, Xiaoyu Chen, Yen-Jen Wang, Shuying Deng, Chenghan Yang, Puzhen Yuan, Chenxin Liu, Tun Ban, Xiang Zhu, Yichen Liu, Kun Feng, Haoang Li, Jianyu Chen
FutureWorlds is a framework that learns robotic world models by generating alternative future predictions and using them as learning signals. It combines candidate construction, history maintenance, and learning from relative quality through a multimodal discrete autoregressive model and diverse beam search. The MemSPO algorithm further optimizes the world model by converting video trajectory rewards into group-relative advantages, leading to significant improvements in LPIPS scores across RT-1, BridgeV2, and RoboCasa datasets.
By Hao Wu, Shengju Qian, Weiyan Wang, Fan Xu, Fan Zhang, Yuanpeng He, Qingsong Wen, Yuxuan Liang
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:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
By Jiakai Huang, Zhongbo Wu, Zheng Zhang, Zihan Wang, Shan You, Tao Huang
arXiv:2606. 17046v1 Announce Type: cross Abstract: Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world.
By Jisang Han, Seonghu Jeon, Jaewoo Jung, Ren\'e Zurbr\"ugg, Honggyu An, Tifanny Portela, Marco Hutter, Marc Pollefeys, Seungryong Kim, Sunghwan Hong
arXiv:2610.33941v1 Announce Type: new
Abstract: World action models have demonstrated promising performance in robot policy learning, yet their high inference latency remains a critical bottleneck fo...
By Guile Wu, Shuangzhi Li, Xingxin Chen, Chengjie Huang, Sam Hosseini, Tongtong Cao, Yingxue Zhang, Dongfeng Bai
The paper introduces Motion-Guided Asymmetric Prediction (MoGAP), a method that enhances World Action Models (WAMs) for robot control by focusing visual supervision on dynamic regions of video data. MoGAP asymmetrically corrupts static and dynamic areas, training the model to reconstruct heavily corrupted dynamic regions using less corrupted static context, all within the shared video diffusion backbone. Experiments on robot manipulation benchmarks show that MoGAP consistently improves policy performance, raising success rates on the GR-1 tabletop benchmark from 38.8% to 47.5% for Fast‑WAM and from 52.3% to 62.3% for Cosmos3‑Nano.
By Taeyoung Kim, Junyoung Sung, Heeseung Kwon, Minseop Kim, Jinwoo Shin
Mem-World introduces a memory‑augmented action‑conditioned world model for robot manipulation, featuring W‑VMem—a 4D wrist‑view‑centered surfel‑indexed memory that anchors historical observations to evolving surface elements. By explicitly modeling when and where scene elements are observed, the system retrieves geometry‑aware history frames during generation, providing informative, non‑redundant context for future action predictions. Experiments demonstrate that Mem‑World produces persistent rollouts, improves policy evaluation reliability (14.5 % higher Pearson correlation with real‑world performance), and boosts long‑horizon task success rates from 58 % to 72 % using synthetic data generation.
By Zirui Zheng, Jiaqian Yu, Xiongfeng Peng, jun shi, Mingyi Li, Chao Zhang, Weiming Li, Dong Wang, Huchuan Lu, Xu Jia