模倣学習のための潜在拡散プランニング
Latent Diffusion Planning for Imitation Learning
行動なしのデモと準最適データを活用するため、潜在空間上で動くプランナーと逆動力学モデルを拡散目的で訓練するLDPを提案し、視覚ロボット操作タスクで既存手法を上回った。
著者: Amber Xie, Oleh Rybkin, Dorsa Sadigh, Chelsea Finn
分類: cs.RO, cs.AI
原文アブストラクト
Recent progress in imitation learning has been enabled by policy architectures that scale to complex visuomotor tasks, multimodal distributions, and large datasets. However, these methods often rely on learning from large amount of expert demonstrations. To address these shortcomings, we propose Latent Diffusion Planning (LDP), a modular approach consisting of a planner which can leverage action-free demonstrations, and an inverse dynamics model which can leverage suboptimal data, that both operate over a learned latent space. First, we learn a compact latent space through a variational autoencoder, enabling effective forecasting of future states in image-based domains. Then, we train a planner and an inverse dynamics model with diffusion objectives. By separating planning from action prediction, LDP can benefit from the denser supervision signals of suboptimal and action-free data. On simulated visual robotic manipulation tasks, LDP outperforms state-of-the-art imitation learning approaches, as they cannot leverage such additional data.
