Hierarchical foresight improves robot manipulation with targeted future predictions
HiFOR: Hierarchical Foresight with Action-Guided World Grounding for Vision-Language-Action Model
Computer Vision and Pattern RecognitionRobotics
Summary
Robots need to understand what might happen next to perform tasks better. The authors of this paper created a new method that helps robots predict short-term changes, key steps, and final goals all together, instead of separately. Their system also organizes these future predictions in a way that makes it easier to find relevant information for current actions. This helps the robot decide what to do more effectively. The new approach was tested on simulated and real-world robot tasks and showed better results.
What this means in practice
- •For robotics engineers: Improve robotic manipulation by integrating hierarchical future predictions to guide real-time action decisions during complex tasks.
- •For automation system developers: Enhance automated systems with better understanding of task progress and relevant future states for adaptive control in dynamic environments.
Authors
Rongxing Ding, Hongyu Qu, Kehong Wu, Xudong Wang, Fan Zhang, Xinguang Xiang, Xiangbo Shu
Abstract
Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integrating visual perception, language understanding, and action generation. Recent advances have further incorporated future prediction to enhance the understanding of environmental dynamics and task progression. However, existing methods often model short-term dynamics and long-term task objectives separately, while directly incorporating predicted futures into action policies without sufficiently considering their relevance to current action requirements. To address these limitations, we propose HiFOR, a hierarchical foresight framework with action-guided world retrieval. Specifically, Hierarchical Foresight Prediction jointly predicts short-term state evolution, semantic milestones, and final goal states through a Hierarchical Dependency Mask (HDM), establishing dependencies across temporal horizons. Building upon these predictions, Structure-Aware Latent World Modeling organizes future latent representations along the horizon, view, and spatial dimensions to facilitate fine-grained retrieval. Furthermore, Action-Guided Factorized World Retrieval adaptively retrieves relevant future information from the structured latent world based on evolving action representations during denoising, providing targeted guidance for action generation. Extensive experiments on two simulation benchmarks and six real-world robotic manipulation tasks demonstrate the effectiveness of our approach.