WAM-OPD introduces a method for post‑training improvement of World Action Models (WAMs) by collecting student rollout histories and querying a stronger teacher for paired video and action targets. The student learns from both modalities while preserving its one‑step generation capability at deployment. Experiments on 12 RoboTwin 2.0 tasks and four real‑robot tasks show success rates rising from 33.8% to 65.7% and from 51.4% to 64.6%, respectively, with joint video‑action supervision outperforming single‑modality approaches.
By Liuhaichen Yang, Zhengyang Zhong, Hanshang Zhu, Ningwei Bai, Qichen Yin, Zhi Han, Jiarui Qin, Zhuang Jiang, Chenchao Sheng, Hanbo Ma, Junkai Liu, Junkai Sun, Dongcheng Lyu, Bo Liu, Yi Dong, Zezhi Tang
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
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
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
The paper introduces NAVA-WAM, a method that learns action priors directly from observation-only videos to pretrain action policies for robots. It uses a two-stage training process: first, it pretrains on videos with future‑video flow‑matching supervision to learn action‑relevant priors, and second, it fine‑tunes with action‑labeled demonstrations for robot control. Experiments show that NAVA‑WAM outperforms previous methods in both in‑distribution and out‑of‑distribution scenarios, achieving strong action‑label efficiency and real‑robot generalization.
By Zhaochong An, Fei Zhang, Menglin Jia, Duncan Frost, Zijian Zhou, Yikai Wang, Xudong Wang, Aditya Patel, Belinda Zeng, Tao Xiang, Serge Belongie, Amir Bar, Sen He
arXiv:2610.07922v1 Announce Type: cross
Abstract: World-action models (WAMs) couple future prediction with robot control, yet existing systems often vary the video backbone, interaction structure, su...
By Heng Yu, David D. Yuan, Juze Zhang, Changan Chen, Yao Feng, Michelle Baldonado, Steve Cousins, Li Fei-Fei, Jiajun Wu, Ehsan Adeli
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
Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cloning. This supervises which motor command was dem...
DyMD introduces a Distribution Matching Distillation framework that adapts teacher supervision and critic fitting to preserve interaction dynamics in few-step video generation. By employing temporal affinity–conditioned re‑noise sampling and dynamics‑guided fake‑score tracking, DyMD balances motion recovery with visual quality. The method distills a 14B teacher into a 1.3B student that achieves significant gains on embodied‑video benchmarks and downstream action planning tasks.
By Haojun Xu, Jie Huang, Xin Lu, Mingchen Zhong, Zihao Fan, Linjiang Huang, Si Liu
WAM-Cache is a training‑free framework that reuses layerwise key‑value (KV) representations from a pretrained video Diffusion Transformer (DiT) across control chunks, recomputing only a sparse set of refreshed tokens. By selecting refresh tokens based on the action expert’s cross‑attention, visual latent surprise, and a strict age bound, it reduces DiT prefill FLOPs by 32‑42% on datasets such as RoboTwin 2.0 and LIBERO while maintaining action accuracy within 0.7‑1.8 percentage points of the dense baseline in simulation and 2.5 points on a real robot.
By Kai Ding, Yang He, Ruijie Quan, Yi Yang
FastOPD is a framework that distills large Vision‑Language‑Action (VLA) models into lightweight versions by using on‑policy distillation with a flow map and a self‑consistency objective. The method trains a compact student to mimic the teacher’s dynamics, achieving performance close to the teacher while drastically reducing inference steps. Experiments on LIBERO, RoboTwin 2.0, and real‑robot deployments show significant latency reductions and higher success rates compared to existing few‑step distillation baselines.
By Yoojin Oh, Jeongsol Kim, Yeonwoo Seo, Jangho Park, Seonghyun Jin, Sunwoo Park, Youngmin Kim, Youngjun Jun, Kyumin Choi, Jong Chul Ye