POD-RoPE: Rotary Position Encoding over Physical Observation Distributions for Embodied AI

POD‑RoPE introduces a rotary position encoding that represents token observation footprints as distributions over camera centers, viewing directions, and sampling times, enabling relative relationships between observations to be captured through expected rotations. The method supports Q/K matching and coherence‑dependent V/O transformations, allowing observation geometry to guide information selection and content aggregation without increasing sequence length. Evaluations across robot policy architectures, simulation, real‑world manipulation, and auxiliary tasks show performance gains and computational efficiency, with significant speedups over existing RoPE variants.

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• robotics
• benchmarks