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[2602.12322] ForeAct: Steering Your VLA with Efficient Visual Foresight Planning
Summary
The paper presents ForeAct, a novel Visual Foresight Planning framework that enhances Vision-Language-Action (VLA) models by enabling them to generate future observations, improving task execution in robotics.
Why It Matters
This research addresses the challenges of executing high-level language instructions in open-world environments, which is crucial for advancing robotics and AI applications. By improving the accuracy and generalization of VLAs, it paves the way for more effective autonomous systems.
Key Takeaways
- ForeAct improves VLA models by generating future observations.
- The framework achieves a significant success rate of 87.4% on diverse tasks.
- It requires no architectural changes to existing VLA systems.
- The foresight generator is pretrained on over 1 million episodes.
- The approach enhances visuo-motor inference over high-level reasoning.
Computer Science > Robotics arXiv:2602.12322 (cs) [Submitted on 12 Feb 2026] Title:ForeAct: Steering Your VLA with Efficient Visual Foresight Planning Authors:Zhuoyang Zhang, Shang Yang, Qinghao Hu, Luke J. Huang, James Hou, Yufei Sun, Yao Lu, Song Han View a PDF of the paper titled ForeAct: Steering Your VLA with Efficient Visual Foresight Planning, by Zhuoyang Zhang and 7 other authors View PDF HTML (experimental) Abstract:Vision-Language-Action (VLA) models convert high-level language instructions into concrete, executable actions, a task that is especially challenging in open-world environments. We present Visual Foresight Planning (ForeAct), a general and efficient planner that guides a VLA step-by-step using imagined future observations and subtask descriptions. With an imagined future observation, the VLA can focus on visuo-motor inference rather than high-level semantic reasoning, leading to improved accuracy and generalization. Our planner comprises a highly efficient foresight image generation module that predicts a high-quality 640$\times$480 future observation from the current visual input and language instruction within only 0.33s on an H100 GPU, together with a vision-language model that reasons over the task and produces subtask descriptions for both the generator and the VLA. Importantly, state-of-the-art VLAs can integrate our planner seamlessly by simply augmenting their visual inputs, without any architectural modification. The foresight generator is pre...
