Vision-language-action policies often struggle to manipulate objects around complex obstacles. We use simulated planning and topological reasoning to generate additional training trajectories, then visually prompt the policy with predicted robot waypoints during deployment. Our method improves performance across simulated and real-world tasks, increasing hardware success by 40% over the strongest baseline.

We introduce Vero, a family of fully open vision-language models (VLMs) designed for general visual reasoning across diverse domains such as charts, science, and spatial understanding. By scaling reinforcement learning (RL) data with the 600K-sample Vero-600K dataset and task-routed rewards, Vero achieves state-of-the-art performance on 30 challenging benchmarks, demonstrating that broad data coverage is the key driver of strong RL scaling. All data, code, and models are publicly released.

We propose a hierarchical input-dependent state space model that leverages the linear scalability of SSMs for decision-making over full-length videos. Our framework couples a temporally consistent visual encoder with an SSM head to propagate temporal information. The temporal module consists of two components: a local aggregation block for fine-grained dynamics and a global relation block for long-range dependencies.