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RynnWorld-Latent is a cross-embodiment video world model that uses 3D-grounded latent actions as a shared conditioning interface, enabling joint pretraining on human videos, robot demonstrations, and simulation rollouts to generate synthetic data that improves robot policy performance on LIBERO and real-world platforms.
RynnVLA-Latent 提出用 30.1K 小时异构视频学习 3D-grounded 潜在动作，预训练 4B 视觉-语言-动作策略，在 LIBERO（98.8%）、RoboTwin 2.0 及真实机器人操作任务上取得领先成功率，无需针对基准的专门调优。
LAWA is a world action model that uses latent actions to enable efficient future imagination for robot control, achieving state-of-the-art performance with reduced inference latency. It improves over baselines in generalization and efficiency without generating future observations.
Latent actions are gaining traction in robotics as a way to learn from unlabeled video without action labels. Recent papers from DeepMind and FAIR demonstrate progress from controlled game environments to in-the-wild internet video, promising scalable training for imitation learning.
PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from mode, improving robotic policy learning performance.
This paper introduces projection agents for graph combinatorial optimization using reinforcement learning and graph neural networks, operating in a continuous action embedding space to improve generalization and scalability, and releases the LaGCO-RL library.
This paper introduces RLA-WM, a visual feature-based world model that leverages residual latent actions and flow matching to efficiently predict future visual states. The method outperforms existing video-diffusion and feature-based approaches while enabling novel robot learning techniques from offline, actionless demonstration videos.