DARE: Diffusion Policy for Autonomous Robot Exploration

TL;DR

DARE introduces a diffusion model-based generative approach for autonomous robot exploration, trained on expert demonstrations to generate effective exploration paths, showing competitive performance and good generalization in various scenarios.

Contribution

It presents a novel diffusion model framework for exploration path planning, leveraging expert demonstrations and attention mechanisms for improved reasoning about unknown environments.

Findings

Achieves on-par performance with state-of-the-art planners.

Demonstrates strong generalization in simulations and real-world tests.

Utilizes ground truth demonstrations for effective training.

Abstract

Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences to reason about unknown areas. In this paper, we propose DARE, a novel generative approach that leverages diffusion models trained on expert demonstrations, which can explicitly generate an exploration path through one-time inference. We build DARE upon an attention-based encoder and a diffusion policy model, and introduce ground truth optimal demonstrations for training to learn better patterns for exploration. The trained planner can reason about the partial belief to recognize the potential structure in unknown areas and consider these areas during path planning. Our experiments demonstrate that DARE achieves on-par performance with both conventional and learning-based state-of-the-art exploration planners, as well as good generalizability in both simulations and real-life scenarios.