PAVXploreRL introduces a reinforcement learning framework that builds on a pretrained latent world model to explicitly optimize Physical Plausibility, Action Adherence, and Visual Fidelity (PAV) objectives. By combining in‑distribution expert trajectories with noise‑driven out‑of‑distribution action exploration, the method avoids reliance on paired video supervision and improves generalization. Experiments demonstrate a 5.6% average performance gain over pretrained baselines and more reliable policy evaluation with reduced overestimation bias.
By Han Wang, Zijun Wang, Shuoshuo Xue, Rui Cao, Fengjiao Chen, Xiaodan Liang, Roy Ka-Wei Lee
Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.
arXiv:2606. 10025v1 Announce Type: cross Abstract: We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution.
By Sriram Krishna, Ben Eisner, Haotian Zhan, Ying Yuan, Haoyu Zhen, Chuang Gan, Shubham Tulsiani, David Held
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:2609.37398v1 Announce Type: new
Abstract: World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning par...
By Xiangcheng Zhan, Zirui Chen, Yicheng Zhao, Ziteng Gao, Shuo Yang
SimWAM is a lightweight World-Action Model that uses future‑video prediction only during training to supervise an action expert, enabling end‑to‑end autonomous driving without costly test‑time future imagination. The architecture co‑trains a pretrained video expert and a lightweight action expert via joint flow matching, while an isolated attention mask keeps action prediction independent of future frames. This design allows the video backbone to be swapped and the action expert scaled independently, achieving 91.5 PDMS on NAVSIM, outperforming state‑of‑the‑art WAM planners with lower latency and zero‑shot transfer to nuScenes.
By Zongchuang Zhao, Xin Zhou, Tianyang Xu, Zhengyang Sun, Kaixuan Zhou, Yu Wu, Honglin Li, Dingkang Liang, Xiang Bai
arXiv:2609.38059v1 Announce Type: cross
Abstract: Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a sca...
By Shenghe Zheng, Wenbo Li, Jiyao Zhang, Bin Xia, Haoyang Huang, Nan Duan, Jiaya Jia
arXiv:2607. 03964v1 Announce Type: cross Abstract: World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience.
By Jianjie Fang, Yongyan Xu, Ziyou Wang, Chen Gao, Yuchao Huang, Zhaolu Wang, Rongze Tang, Mingyuan Jia, Baining Zhao, Weichen Zhang, Xin Zhang, Haisheng Su, Yu Shang, Wei Wu, Xinlei Chen, Yong Li
arXiv:2606. 11628v1 Announce Type: cross Abstract: The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments.
By Harsh Gupta, Guanya Shi, Wenzhen Yuan
arXiv:2502. 18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models.
By Will Schwarzer, Jordan Schneider, Philip S. Thomas, Scott Niekum
arXiv:2609.27455v1 Announce Type: new
Abstract: World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained Video Diffusion Models (VDMs). I...
By Xueji Fang, Boqiang Duan, Hua Wu, Jingdong Wang, Guo-Jun Qi
arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.
By Zifan Xu, Ran Gong, Maria Vittoria Minniti, Kausik Sivakumar, Ahmet Salih Gundogdu, Eric Rosen, Riedana Yan, Tushar Kusnur, Zixing Wang, Di Deng, Peter Stone, Xiaohan Zhang, Karl Schmeckpeper