arXiv:2608. 09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation.
By Peterson Co, Sicheng Hu, Chunxuan Jiao, Hongyang Cheng, Yulin Luo, Yijie Xu, Sixiang Chen, Zhongxia Zhao, Zihao Wang, DaFeng Chi, Peidong Liu, YuTong Chen, Henghua Liu, Zhihao Yuan, Huizhu Jia, Yuzheng Zhuang, Tianle Zhang, Liang Lin, Huajie Tan, Shanghang Zhang
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
Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this promise requires precise action-conditioned transitions rather than merely plausible outputs.
The paper introduces a metric interaction framework for robotic manipulation that explicitly models object- and scene-level interactions in Cartesian space. It uses Interaction‑Centric Tokens (ICTs) to represent end‑effector trajectories relative to objects and a Metric Action Interaction Field (MAIF) to attend to scene point‑cloud features for geometry‑conditioned action corrections. Experiments show modest but consistent improvements across several benchmarks, including LIBERO, RoboTwin 2.0, and real‑world tasks.
By Lijie Wang, Zheng Lu, Yiming Wang, Heyang Yu, Kenghou Hoi, Bowen Hu, Di Cui, Tianyu Xin, Haoran Liao, Wanqi Zhong, Xingjie Fan, Yizhao Xu, Ziliang Wang, Fei Gao, Yiming Li
The paper introduces HF‑WM, an action‑conditioned world model that learns from large‑scale egocentric human videos and incorporates failure dynamics through post‑training. It bridges the embodiment gap with a dual‑branch framework that couples video generation to stabilized pointflow prediction, unifying motion representation across humans and robots while reducing egocentric camera motion. Failure modeling is enhanced by Group Relative Policy Optimization on perturbed actions, guided by action alignment and physical plausibility rewards, enabling the model to anticipate unsuccessful executions. Experiments show HF‑WM outperforms existing methods in action‑conditioned video generation and is useful for evaluating policies in downstream robotic tasks.
By Peiyan Tu, Cong Wang, Hanxin Zhu, Xin Jin, Zhibo Chen
arXiv:2609.40219v1 Announce Type: cross
Abstract: World Action Models (WAMs) couple visual dynamics prediction with action generation, yet they do not explicitly support the reuse of action experienc...
By Qi Lyu, Jiahua Dong, Hao Shen, Xudong Wang, Hongyuan Yu, Baichen Liu, Henghui Ding, Zhi Han, Nicu Sebe, Ivan Laptev, Fahad Shahbaz Khan, Salman Khan