$π\mathbf{R}^2$: Reactive Real-time Flow Policies

Imagine you're building a robotic arm that needs to adapt quickly to changes in its environment while performing tasks. Current manipulation policies often rely on large pretrained models that execute actions in chunks without the ability to adjust mid-task, leading to issues like stale actions and slow response times. This is particularly problematic in dynamic settings where quick adjustments are crucial, a situation known as latency. When the system can't react to new sensory input, it can miss opportunities or make mistakes, which is a significant limitation for real-time applications.

To address this, the authors propose a method called πR², which allows these policies to be more reactive while still leveraging the power of large pretrained models. The key idea is to separate the input channels: one channel processes fast, real-time proprioceptive data, while another handles slower, asynchronously updated visual information. This way, the policy can respond to immediate changes in its position while still using the broader context from visual inputs. Additionally, the method adapts to varying hardware latencies, allowing for quicker replanning and action execution. Compared to existing methods, πR² achieves a roughly fourfold increase in replanning speed and significantly improves success rates in both simulated and real-world tasks, making it a more effective solution for dynamic control scenarios.

The approach introduces a novel way to enhance reactivity in manipulation policies.

The results show significant improvements in both simulation and real-world tasks with solid baselines.