Sidekick Policy Learning for Active Visual Exploration

TL;DR

This paper introduces sidekick policy learning for active visual exploration, enabling agents to efficiently reconstruct environments with limited views by leveraging preparatory learning phases and novel visualization techniques.

Contribution

It proposes a new sidekick policy learning framework that improves exploration efficiency and convergence, supported by a novel visualization method.

Findings

Sidekick learning improves exploration performance.

Method accelerates convergence compared to existing approaches.

Effective in 360 scenes and 3D object exploration tasks.

Abstract

We consider an active visual exploration scenario, where an agent must intelligently select its camera motions to efficiently reconstruct the full environment from only a limited set of narrow field-of-view glimpses. While the agent has full observability of the environment during training, it has only partial observability once deployed, being constrained by what portions it has seen and what camera motions are permissible. We introduce sidekick policy learning to capitalize on this imbalance of observability. The main idea is a preparatory learning phase that attempts simplified versions of the eventual exploration task, then guides the agent via reward shaping or initial policy supervision. To support interpretation of the resulting policies, we also develop a novel policy visualization technique. Results on active visual exploration tasks with 360 scenes and 3D objects show that sidekicks consistently improve performance and convergence rates over existing methods. Code, data and demos are available.