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

TL;DR

This paper introduces F2F-AP, a flow-to-future asynchronous policy that predicts future observations to compensate for latency in real-time dynamic robotic manipulation, improving responsiveness and success.

Contribution

It proposes a novel flow-based prediction framework with contrastive learning to enable proactive planning in asynchronous robotic manipulation policies.

Findings

Significantly improves responsiveness in dynamic manipulation tasks.

Enhances success rates in complex, moving-object scenarios.

Effectively compensates for inherent latency in asynchronous inference.

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.