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Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies

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Briefings

A research paper introduces Execution-Aligned Progressive Noise (EAPN) to improve asynchronous replanning in generative robot policies. The method aligns stochastic noise with actual execution displacement, enabling consistent continuation across action chunks. Evaluated on D3IL, Kinetix, LIBERO, and real-world manipulation tasks, EAPN achieved an 88.59% success rate on Kinetix and maintained robust performance under long inference delays.

Key points

• EAPN introduces structured stochasticity at both inter-chunk and intra-chunk levels to align noise with actual execution displacement.
• EAPN achieved an 88.59% success rate on the Kinetix benchmark and maintained strong performance under long inference delays.

Context

Independent stochastic initialization in generative robot policies causes mode switching and inconsistent continuation across action chunks. EAPN addresses this by propagating a shared noise trajectory aligned with execution displacement.

Limitations

Evaluation is limited to D3IL, Kinetix, LIBERO, and real-world manipulation tasks. Performance on Object Storage and bimanual Cloth Folding was reported as 90.0% and 96.7% respectively, but specific inference delay constraints for these tasks are not detailed in the evidence.

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