Dynamic Execution Horizon Prediction for Chunk-based Robot Policies

• URL: http://arxiv.org/abs/2606.11408v1
• Date: Tue, 09 Jun 2026 19:58:31 GMT
• Title: Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
• Abstract summary: Action chunking has become a standard design in modern robot policies, where the policy predicts a sequence of actions and executes a fixed number of them instead of acting one step at a time.<n>In practice, the execution horizon is typically chosen through empirical tuning and is highly task-dependent.<n>We propose Dynamic Execution Horizon Prediction (DEHP), an effective method that trains a lightweight execution-horizon prediction branch using online reinforcement learning.
• Score: 29.482987292744568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action chunking has become a standard design in modern robot policies, from diffusion/flow policies to vision-language-action models, where the policy predicts a sequence of actions and executes a fixed number of them instead of acting one step at a time. However, this paradigm relies on a key assumption: a fixed execution horizon. During chunk execution, the policy operates open-loop, which is particularly problematic for fine-grained manipulation tasks that require frequent replanning. In practice, the execution horizon is typically chosen through empirical tuning and is highly task-dependent. To this end, we propose Dynamic Execution Horizon Prediction (DEHP), an effective method that trains a lightweight execution-horizon prediction branch using online reinforcement learning while keeping the pretrained chunk policy completely frozen. This makes the method compatible with black-box chunk policies and isolates the effect of adapting the execution horizon from changes to the underlying action generator. Across our evaluations, DEHP improves the success rate of different high-precision and long-horizon manipulation tasks by a large margin. Our qualitative analysis further shows that DEHP predicts shorter execution horizons during fine-grained stages of the task and longer horizons during free-space motion. In this way, DEHP balances the efficiency of open-loop chunk execution with the reactivity of closed-loop single-step control. Project page: https://dehp-chunking.github.io/

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