F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation
For robotic manipulation in dynamic environments, this work solves the latency-induced temporal misalignment, enabling proactive planning and robust execution with actively moving objects.
The paper addresses the latency problem in asynchronous robotic manipulation, where actions lag behind real-time environments. It proposes a framework using predicted object flow to synthesize future observations, achieving significant improvements in responsiveness and success rates in dynamic tasks.
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.