arXiv:2609.22332v1 Announce Type: cross
Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...
By Jiadi You, Qize Yu, Yue Chen, Minghong Cai, Zhide Zhong, Yuran Wang, Bowen Ping, Jiaqi Liang, Zhenhao Shen, Haodong Yan, Yinchuan Li, Ruihai Wu, Xiaojuan Qi, Yingcong Chen
arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.
By Jiawei Zhang, Kaizhe Hu, Yingqian Huang, Yuanchen Ju, Zhengrong Xue, Huazhe Xu
RoboCousin is an extensible simulation platform that transforms user-provided observations into reusable assets, scenes, and expert trajectories for bimanual robotic manipulation. It converts object images into simulation-ready models with visual, collision, semantic, and physical metadata, automatically generates grasp candidates, and builds digital cousins that vary objects, backgrounds, layouts, and language instructions while preserving task-relevant affordances. The platform supports both tabletop and room-level scene construction, and the authors release RoboCousin-OBD with over 3,000 annotated objects and 50 backgrounds, generating more than one million expert trajectories across 50 tasks, with simulation and real-robot experiments demonstrating comparable annotation quality and effective sim-to-real transfer.
By Jingxuan Zhu, Jingyi Li, LiangLiang Chen, Zhiyuan Jing, Jidong Zhang, Hongming Li
arXiv:2606.17446v2 Announce Type: replace-cross
Abstract: Simulation enables scalable robot data collection, but raw 3D assets provide only geometry, lacking the semantic, interactive, and physical k...
By Haoran Lu, Mutian Shen, Shuyang Yu, Yu Xiao, Songling Liu, Jianshu Zhang, Shang Wu, Yue Chen, Guo Ye, Jiayi Wang, Zhaoran Wang, Han Liu
arXiv:2512.04231v2 Announce Type: replace-cross
Abstract: Assistive robots operating in unstructured environments must reason not only about what objects are, but how they can be used, including non-...
By Zhou Chen, Joe Lin, Carson Bulgin, Sathyanarayanan N. Aakur
ForeAct3D is a framework that grounds future world modeling in Vision‑Language‑Action policies by decoding depth, semantic segmentation, and camera pose from policy representations into current and future 3D scene states. The future queries are conditioned on the policy‑generated action chunk, and a physical‑consistency closure enforces background staticity, instance‑level rigidity, and wrist‑camera pose alignment with end‑effector kinematics. This approach eliminates the need for future prediction at inference and achieves high success rates on LIBERO and CALVIN, outperforming the base policy across all suites.
By Zhe Tao, Feiran Wang, Gaowen Liu, Ramana Rao Kompella, Yan Yan