MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.
By Yiguang Yang, Jiankun Peng, Xiaoming Wang, Yiran Zhang, Zhibo Fang
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: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
arXiv:2606.27504v2 Announce Type: replace
Abstract: World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only...
By Tianze Xia, Lijun Zhou, Kaixin Xiong, Jingfeng Yao, Zhenxin Zhu, Haiyang Sun, Bing Wang, Guang Chen, Wenyu Liu, Hangjun Ye, Xinggang Wang
arXiv:2609.37250v1 Announce Type: cross
Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrai...
By Yang Zhang, Jiangyuan Zhao, Chenyou Fan, Jiayu Hu, Xiu Yuan, Chenjia Bai, Xiu Li
arXiv:2609.39324v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models have recently incorporated world models to provide richer dynamic supervision beyond sparse action labels. Howeve...
By Jingqiu Wang, Yan Wang
arXiv:2610.28122v1 Announce Type: cross
Abstract: Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pre...
By Shaoyi Zheng, Zhongtao Jiang, Yue Peng, Wenbo Lu, Shunchi Zhang, Xi Wang, Shengji Wan
arXiv:2610.26903v1 Announce Type: new
Abstract: Action-conditioned world models support planning and policy evaluation by predicting future states. However, learning an accurate one-step predictor ca...
By Adam Lee, Shobhit Agarwal, June Pyo Suh
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
arXiv:2609.36645v1 Announce Type: cross
Abstract: Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encou...
By Hanseul Kim, Jewon Yeom, Youngjoon Jeong, Minsoo Jo, Taesup Kim
CtrlWAM introduces a controllable world action model that jointly predicts actions (intent) and visual futures (foresight). By executing perturbed actions in a simulator and pairing them with noised visual outcomes, it aligns action predictions with their visual consequences, using warped video–action noise schedules to maintain visual layout responsiveness. The model extends beyond ego‑only control to multiple agent streams, improving action forecasts, video–action agreement, and command following in driving and robotics experiments.
By Chensheng Peng, Wenhao Ding, Ran Tian, Zewei Zhou, Jef Packer, Maximilian Igl, Peter Karkus, Yan Wang, Masayoshi Tomizuka, Boris Ivanovic, Marco Pavone, Yuxiao Chen
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