Recent advances in visuospatial understanding and agentic reasoning enable direct robotic manipulation with general-purpose multimodal models, but their embodied capabilities remain poorly understood. This paper systematically evaluate GPT-6 Astra for closed-loop robotic manipulation using a lightweight agent execution...
You-Wei Zhou, Bo-Han Hou, Yan-Kai Fu et al.· 0 citations
RoboDreamer is presented, a two-stage teacher--student framework that combines next-observation consistency with randomized continuous temporal masking that demonstrates robust motion tracking under observation masking and successful real-world deployment.
Zhe Li, Yang-Yang Wei, Xi-Chen Yuan et al.· 0 citations
LAW, a closed-loop VLM--WAM framework that unifies language reasoning, action generation, and world modeling through semantic subtask progress, is presented, establishing a new open-source state of the art on RoboTwin.
Jia-Jun Cao, Kai Tang, Bo-Hua Zhang et al.· 0 citations
The ActiveArena-Sim, an active-perception simulator with controllable viewpoints and large-scale workspaces as the foundation, is introduced, and ActiveArena-Bench is proposed, which comprises 35 tasks across 5 fine-grained categories, covering visual exploration and interactive information acquisition.
Yi-Bo Li, En-Shen Zhou, Rui Chen et al.· 1 citation
WorldEcho is introduced, which probes action following over a broader action distribution using visual integrity and SE(3) trajectory alignment, and WorldSync is proposed, which strengthens action following along three complementary axes: distributional coverage, representational grounding, and intervention-effect alig...
Real-world experiments demonstrate that a single $\omega$-0 model can produce smooth manipulate-while-moving behaviors and consistently outperform representative imitation learning, VLA, humanoid, and WAM baselines.
This work formalizes Observable Simulator Contract, a minimal contract that any action-conditioned physical simulator should satisfy: supplied actions must induce corresponding agent motion, and environment responses must be grounded in that realized motion.
P. Co, Sichen Hu, Chun-Xuan Jiao et al.· 1 citation
This work introduces Robo-Dopamine 2.0, a history- and OOD-aware process reward model with a pairwise prediction interface that combines history-conditioned pairwise rewards that use source-aligned reference panels for synthetic OOD queries and observed rollout history for online queries, while preserving the queried e...
Yijie Xu, Hao-Peng Jin, Run Zhou et al.· 2 citations
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