Revisiting Open-Loop Execution in Robotics: Toward Reactive, Higher-Performing Policies

• URL: http://arxiv.org/abs/2608.15938v2
• Date: Wed, 19 Aug 2026 16:19:18 GMT
• Title: Revisiting Open-Loop Execution in Robotics: Toward Reactive, Higher-Performing Policies
• Abstract summary: Action chunking has emerged as a key enabler of recent progress in imitation learning for robotic manipulation.<n> executing long open-loop prefixes reduces reactivity, limiting policies' ability to correct for errors.<n>We argue that long open-loop execution primarily helps short-context policies imitate "non-Markovian demonstrations"
• Score: 21.50347781480984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action chunking --- the practice of predicting a sequence of actions and executing a prefix open-loop --- has emerged as a key enabler of recent progress in imitation learning for robotic manipulation. However, executing long open-loop prefixes reduces reactivity, limiting policies' ability to correct for errors. Further, the mechanisms underlying these performance benefits remain poorly understood: prior works cite mitigating compounding errors, absorbing inference latency, or smoothing motions, but provide limited controlled evidence or guidance for preserving reactivity. In this work, we argue that long open-loop execution primarily helps short-context policies imitate "non-Markovian demonstrations". Across four simulation and two real-world tasks, we show that expert non-Markovianity strongly shapes the relationship between task success and open-loop execution horizon. Further, we investigate the impact of compounding errors --- the prevailing explanation for long open-loop execution in prior work --- and find that while they matter, expert non-Markovianity has a much stronger impact in our experimental setting. Finally, we show that when policies are provided with a sufficiently long context, open-loop execution is no longer beneficial and the most reactive, closed-loop policies perform best. While imitation learning has seen great success using long open-loop execution, our findings motivate long-context, reactive policies as a more principled and performant paradigm.

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