Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning

TL;DR

This paper introduces Interaction-Merged Motion Planning (IMMP), a novel method that effectively leverages diverse motion datasets for robust autonomous robot planning by capturing and transferring agent interactions across domains.

Contribution

IMMP is a two-step domain adaptation approach that pre-merges source datasets to capture interactions and then merges them into an adaptable model, addressing domain imbalance and reducing computational costs.

Findings

IMMP outperforms conventional domain adaptation methods on multiple benchmarks.

The approach effectively captures diverse agent interactions for robust planning.

IMMP reduces catastrophic forgetting and computational costs.

Abstract

Motion planning is a crucial component of autonomous robot driving. While various trajectory datasets exist, effectively utilizing them for a target domain remains challenging due to differences in agent interactions and environmental characteristics. Conventional approaches, such as domain adaptation or ensemble learning, leverage multiple source datasets but suffer from domain imbalance, catastrophic forgetting, and high computational costs. To address these challenges, we propose Interaction-Merged Motion Planning (IMMP), a novel approach that leverages parameter checkpoints trained on different domains during adaptation to the target domain. IMMP follows a two-step process: pre-merging to capture agent behaviors and interactions, sufficiently extracting diverse information from the source domain, followed by merging to construct an adaptable model that efficiently transfers diverse interactions to the target domain. Our method is evaluated on various planning benchmarks and models, demonstrating superior performance compared to conventional approaches.