Scaling Motion Forecasting Models with Ensemble Distillation for Efficient Deployment on Autonomous Robots

Ensemble models can significantly improve the accuracy of motion forecasting systems, but their high computational cost makes them impractical for deployment on autonomous robots. This work develops a framework to distill large ensembles into smaller student models that retain high performance at a fraction of the compute cost, enabling efficient deployment on resource-constrained robotic platforms.