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
The paper introduces a reinforcement learning post‑training scheme that trains robot world models on their own autoregressive rollouts, using a contrastive RL objective adapted from diffusion models. It also proposes a training protocol that compares multiple variable‑length futures, a multi‑view visual fidelity reward, and demonstrates state‑of‑the‑art rollout fidelity on the DROID dataset, outperforming baselines on LPIPS, SSIM, and human preference tests.
By Jai Bardhan, Patrik Drozdik, Josef Sivic, Vladimir Petrik
PACT‑WAM is a world‑action model that simultaneously predicts a 16‑step action trajectory and its corresponding visual forecast for robot manipulation. It uses a hierarchical history encoder that compresses past observations into fewer tokens, reducing processing cost by 75% compared to dense encoding. The model’s shared flow module updates action and visual states jointly, and a TiTok‑VAE decoder reconstructs multi‑view future images, which are then used by a vision‑language component (Proposal Review) to improve execution‑prefix selection and proposal rejection, boosting success rates on several benchmarks.
By Yushan Liu, Jingjing Fan, Shoujie Li, Yifan Xie, Xiao-Ping Zhang, Wenbo Ding
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem
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: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