Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection

2026-06-18

AI Summary

This work addresses the challenge of action boundary inconsistency in asynchronous robotic execution, where delays in generating action chunks disrupt the continuity of real-time control. To resolve this issue without retraining, gradient computation, or policy modification, the authors propose PAINT—a training-agnostic method that reframes the problem as an initial noise selection task. By leveraging backward Euler inversion and a redrawing rule, PAINT enables an unmodified flow ODE to directly synthesize future action chunks consistent with the already executed action prefix. Evaluated across 12 simulation benchmarks and 6 real-world manipulation tasks, the approach significantly improves action consistency and task success rates, demonstrating broad applicability on single-arm, dual-arm, and humanoid robotic platforms.

Abstract

Action chunking enables robot policies to produce temporally coherent behavior, but generating multi-step action sequences with flow-based policies incurs latency that is incompatible with real-time control. Under asynchronous execution, the robot continues executing the current chunk while the next one is generated, causing even minor delays to create inconsistencies at chunk boundaries. Existing methods address this problem by steering generation toward the already executed action prefix. We instead show that prefix consistency can be achieved by selecting an appropriate initial noise before generation begins, allowing the unmodified flow ODE to produce a coherent next chunk. This reframes asynchronous inference as a noise selection problem rather than a trajectory steering problem. We introduce \textbf{PAINT}, a training-free method that finds this noise via backward Euler inversion and constructs the final chunk through a repainting rule. In summary, \texttt{PAINT} requires no gradients, retraining, or policy modification; yet it improves execution consistency and task performance across \textit{12 simulated benchmarks} and \textit{6 real-world manipulation tasks} spanning single-arm, bimanual, and humanoid embodiments. Website: ~\href{https://paint-action-chunking.github.io}{\texttt{https://paint-action-chunking.github.io}}.

Problem

• action chunking
• asynchronous execution
• temporal consistency
• real-time control
• flow-based policies

Innovation

• asynchronous execution
• initial noise selection
• action chunking
• flow-based policy
• training-free method