Compact AI Training Method Reduces Navigation Conflicts in Crowded Environments
A paper accepted to ECCV 2026 by researchers at DGIST and KAIST identifies Skill Conflict, a phenomenon in which motion planning and motion prediction compete for the same weights inside the compact shared encoders that mobile robots run. Their fix, Disjoint Parameter Training with Sparse Merging, cut the JRDB collision rate from 0.0189 to 0.0091 on a 6.44 million parameter model. We read the paper, the supplementary tables and the ablations, including the three-way task split that made results worse.