Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training

Abstract

Accurate motion prediction of surrounding agents and safemotion planning are two closely coupled key tasks for social robot nav-igation in crowded environments. Deploying these systems on resource-constrained edge devices necessitates compact, unified models that canperform both tasks simultaneously. However, within these compact sharedencoders, recent unified models often overlook severe representationalconflicts that arise from the distinct objectives of predicting neighbor be-haviors versus ego-centric safety planning. To address this issue, we firstidentify the Skill Conflict—a phenomenon where overlapping parameterassignments cause distinct tasks to compete for the same weights, pre-venting the model from fully specializing in individual skills. To resolvethis, we propose a novel model-merging-based framework, Disjoint Pa-rameter Training (DPT). DPT mitigates performance degradationcaused by Skill Conflict through distributed parameter learning, whichseparates the key parameter regions of each task while preserving theircore capabilities prior to merging. In addition, we observe that sparsemerging, which selectively integrates only the most influential param-eters for each task rather than combining all task-specific parameters,yields optimal performance by preventing interference among adjacentfeatures and concentrating representational capacity. DPT can be ap-plied in parallel with a variety of merging methods. Evaluated on stan-dard crowd navigation benchmarks (JRDB and JTA), our frameworkdemonstrates superior performance, validating its versatility and effec-tiveness for safe, resource-efficient robot navigation. The project page isavailable at: https://dpt2026.github.io/