Kinematic Hypothesis Context: Embodiment-Conditioned Learned Video Coding for Bandwidth-Limited Robot Policy Uplinks

1. 05:00
   arXiv Robotics — research abstractsSignal

   Kinematic Hypothesis Context: Embodiment-Conditioned Learned Video Coding for Bandwidth-Limited Robot Policy Uplinks

   This research addresses the bandwidth bottleneck of camera uplinks when robots run large vision-language-action policies on remote servers. The authors introduce a 'kinematic hypothesis context,' a zero-rate conditioning signal derived from joint states, robot models, and camera calibration. By incorporating ego-motion homographies, per-link reprojections, and background memory warps into a learned video codec, the method reduces redundant bit usage for predictable robot motion. Experimental results on the LIBERO benchmark show a 23.9% reduction in bit rate compared to history-conditioned codecs and 40-56% over standard codecs like x264 and SVT-AV1. In closed-loop tests with OpenVLA-OFT, success rates nearly doubled from 22.8% to 43.1% at 6 kbps under packet loss conditions, outperforming DCVC-RT by 23.6 points. The approach generalizes across different codec hosts and real robot data.

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