New $\pi\mathbf{R}^2$ method boosts robotic policy reactivity

Researchers have developed $\pi\mathbf{R}^2$, a novel method to enhance the reactivity of large, pretrained manipulation policies in real-time robotic control. This approach addresses the latency issue in current action-chunking policies by splitting sensory input into fast and slow channels, allowing the policy to react to immediate proprioceptive data while tolerating delays in vision processing. $\pi\mathbf{R}^2$ also adapts to varying hardware latencies by treating in-flight actions as inpainting conditioning, enabling faster replanning and improving task success rates. AI

IMPACT Enhances real-time robotic control by improving the reactivity of large manipulation policies, potentially leading to more dynamic and successful task execution.

RANK_REASON Publication of a new research paper detailing a novel method for robotic control policies. [lever_c_demoted from research: ic=1 ai=1.0]

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• GR00T-N1.7
• $\pi\mathbf{R}^2$
• xArm6+XHand

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