Sidekick Policy Learning for Active Visual Exploration
This addresses the challenge of efficient environment reconstruction for agents in robotics or VR, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of active visual exploration, where an agent must select camera motions to reconstruct environments from limited glimpses, by introducing sidekick policy learning to improve performance and convergence rates, showing consistent gains over existing methods.
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.