STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience

TL;DR

STERLING introduces a self-supervised learning method enabling robots to recognize and navigate different terrains using only unlabeled, unconstrained experience, matching or surpassing supervised methods in real-world off-road tasks.

Contribution

It presents a novel multi-modal self-supervised approach for terrain representation learning that requires no labeled data or expert demonstrations.

Findings

STERLING features match supervised approaches in terrain recognition.

Outperforms state-of-the-art methods in preference-aligned visual navigation.

Successfully completes a 3-mile off-road hike with minimal manual intervention.

Abstract

Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation. Current approaches that provide robots with this awareness either rely on labeled data which is expensive to collect, engineered features and cost functions that may not generalize, or expert human demonstrations which may not be available. Towards endowing robots with terrain awareness without these limitations, we introduce Self-supervised TErrain Representation LearnING (STERLING), a novel approach for learning terrain representations that relies solely on easy-to-collect, unconstrained (e.g., non-expert), and unlabelled robot experience, with no additional constraints on data collection. STERLING employs a novel multi-modal self-supervision objective through non-contrastive representation learning to learn relevant terrain representations for terrain-aware navigation. Through physical robot experiments in off-road environments, we evaluate STERLING features on the task of preference-aligned visual navigation and find that STERLING features perform on par with fully supervised approaches and outperform other state-of-the-art methods with respect to preference alignment. Additionally, we perform a large-scale experiment of autonomously hiking a 3-mile long trail which STERLING completes successfully with only two manual interventions, demonstrating its robustness to real-world off-road conditions.