Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training
Accurate motion prediction of surrounding agents and safe motion planning are two closely coupled key tasks for social robot navigation in crowded environments. Deploying these systems on resource-constrained edge devices necessitates compact, unified models that can perform both tasks simultaneously. However, within these compact shared encoders, recent unified models often overlook severe representational conflicts that arise from the distinct objectives of predicting neighbor behaviors versus ego-centric safety planning. To address this issue, we first identify the Skill Conflict$\unicode{x2014}$a phenomenon where overlapping parameter assignments cause distinct tasks to compete for th...
| Authors | Taewon Seo, Seonae Jeon, Giwon Lee et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 July 2026 |
| Last updated at source | 27 August 2026 |
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