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About this paper
Kaixi Feng, Guoheng Sun, Ang li
PAIR adds a perception-derived bridge to continuous-action robot policies, making task-relevant information shaped by expert action chunks available before action refinement. During training, an action autoencoder supplies the bridge’s action-structure target; at inference, the bridge uses the current observation and instruction. On seven physical-robot tasks, OpenVLA-OFT’s macro-average success rose from 51.4% to 65.0%, limited to this evaluated setting.
Summary by alphaXiv.
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