Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
This addresses the problem of ambiguous goal inference in imitation learning for AI agents, though it is incremental as it builds on existing reward learning methods.
The paper tackles one-shot imitation learning for object rearrangement by proposing Graph-based Equivalence Mappings (GEM) to infer spatial goal specifications from a single demonstration, enabling successful generalization to unseen environments and showing drastic improvements over baselines.
In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same task in different environments. To achieve a strong generalization, the AI agent must infer the spatial goal specification for the task. However, there can be multiple goal specifications that fit the given demonstration. To address this, we propose a reward learning approach, Graph-based Equivalence Mappings (GEM), that can discover spatial goal representations that are aligned with the intended goal specification, enabling successful generalization in unseen environments. Specifically, GEM represents a spatial goal specification by a reward function conditioned on i) a graph indicating important spatial relationships between objects and ii) state equivalence mappings for each edge in the graph indicating invariant properties of the corresponding relationship. GEM combines inverse reinforcement learning and active reward learning to efficiently improve the reward function by utilizing the graph structure and domain randomization enabled by the equivalence mappings. We conducted experiments with simulated oracles and with human subjects. The results show that GEM can drastically improve the generalizability of the learned goal representations over strong baselines.