F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation

2026-04-02

AI Summary

This work addresses the challenge of action latency in asynchronous robotic manipulation, where delays between perception and execution hinder responsiveness to rapidly changing environments. To mitigate this issue, the authors propose a novel approach that integrates optical flow prediction with contrastive learning. By synthesizing future observations and aligning the visual features of predicted and actual future states through optical-flow-guided contrastive learning, the policy acquires forward-looking planning capabilities that compensate for system delays. This method represents the first integration of optical flow prediction and contrastive learning for modeling future states in dynamic scenes, significantly enhancing the response speed, success rate, and robustness of asynchronous policies in complex tasks involving moving objects.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation, achieving significant progress in ensuring trajectory smoothness and efficiency. However, a systemic challenge remains unresolved, as inherent latency causes generated actions to inevitably lag behind the real-time environment. This issue is particularly exacerbated in dynamic scenarios, where such temporal misalignment severely compromises the policy's ability to interpret and react to rapidly evolving surroundings. In this paper, we propose a novel framework that leverages predicted object flow to synthesize future observations, incorporating a flow-based contrastive learning objective to align the visual feature representations of predicted observations with ground-truth future states. Empowered by this anticipated visual context, our asynchronous policy gains the capacity for proactive planning and motion, enabling it to explicitly compensate for latency and robustly execute manipulation tasks involving actively moving objects. Experimental results demonstrate that our approach significantly enhances responsiveness and success rates in complex dynamic manipulation tasks.

Problem

• asynchronous inference
• latency
• dynamic manipulation
• temporal misalignment
• real-time robotics

Innovation

• asynchronous policy
• object flow prediction
• future observation synthesis
• contrastive learning
• dynamic manipulation