Aug 2026· 7 citations· ⚡ 1 influential· 50 references
Computer Science
Abstract
We introduce Hydra-0, a generalist world model conditioned on action flow, which represents robot actions as pixel motion. This shared visual interface enables generalist world modeling and control by learning action consequences across embodiments, tasks, environments, and video-generation backbones. Our best configuration achieves 90.4% lower robot-motion error and 60.2% lower object-motion error than our action-conditioned baseline, while supporting zero-shot composition and data-efficient adaptation. On the RoboLab benchmark, Hydra-0 achieves a Pearson correlation of r=0.96 between replayed and reference success rates. Finally, we uncover an emergent inverse mode of this interface: a world action model that predicts compatible robot motion from desired object flow transferred from a human demonstration. A trained action head maps the resulting latent features to executable actions without requiring task-specific expert robot demonstrations. Together, these results demonstrate the potential of action flow as a shared control interface connecting heterogeneous training data, open-loop policy evaluation, and robot control.
GeniWorld is presented, an interactive world model for robots that generalizes robustly across unseen scenarios by explicitly decoupling embodiment kinematics from environmental dynamics, and generates diverse manipulation trajectories within the world model, improving downstream policy performance and robustness in co...
JoyAI-RA 0.5 is proposed, a generalist Vision-Language-World-Action framework that couples physical world-dynamics priors with visual semantics and scales manipulation learning across such data via dual action alignment, suggesting that abundant but weakly labeled human experience can be converted into a transferable t...
SelfWAM is introduced, a unified self-grounded WAM built on a modality-specialized Mixture-of-Transformers (MoT) architecture that jointly predicts actions, action-conditioned future RGB frames, and robot self-masks, thereby grounding future prediction in the robot's visible body and its action-induced motion.
Bikang Pan, Fan Liu, Haotao Lu et al.· 5 citations
LingBot-VA 2.0 is presented, a video-action foundation model built from the ground up for embodiment, which introduces a semantic visual-action tokenizer, which aligns visual representations with both semantics and actions, improving instruction following and action precision in subsequent policy learning.
Qihang Zhang, Lin Li, Luyao Zhang et al.· arXiv.org· 11 citations· ⚡3
LM-X is introduced, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence and shows that explicit multi-timescale predictive state can strengthen control while exposing interpretable internal estimates.
Jin Lou, Zhi Jing, Xu-Peng Wang et al.· 0 citations
Results demonstrate that unified World Action Modeling together with progressive embodied pretraining effectively transforms large-scale embodied experience into generalizable robot manipulation capabilities.
Hao Sun, Jiangbo Pei, Fei Kang et al.· 0 citations
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