Genie Envisioner Act 2.0 (GE-Act 2.0) is introduced, a world-action model whose trainable generative and action components are all initialized from scratch on manipulation data, and its components are jointly trained with knowledge-aligned selective optimization (KASO), which reduces mismatched supervision.
AgiBot Research Team, Ren-Hang Liu, Wen-Zhi Zhao et al.· 4 citations· ⚡1
This work presents RoboInter1.5, an extended and holistic suite of intermediate representations for both robotic manipulation and embodied world modeling, and introduces RoboInter-World, which leverages intermediate representations as structured conditioning signals for controllable prediction of future world states.
Ziqin Wang, Hao Li, Weijun Wang et al.· arXiv.org· 0 citations
The Kinematic-Aware Articulation Interface (KAI), a structured intermediate representation that captures the kinematic structure of articulated objects, is introduced, a structured intermediate representation that provides a strong inductive bias aligned with the underlying structure of articulated motion.
Ya-Ping Li, Zhaxizhuoma, Qiao-Jun Yu et al.· arXiv.org· 0 citations
InternVLA-A1.5 is presented, which builds the policy on a native VLM backbone that keeps training on VQA and subtask prediction, and attaches a lightweight unified expert for continuous action generation, and achieves the best overall results on all six simulation benchmarks.
