残缺条件下基于生成式轨迹建模的鲁棒模仿学习方法研究OA

Efficiently leveraging imperfect expert demonstration data is one of the key challenges in the field of imitation learning.Imperfect demonstrations,which entail the loss of fine-grained local details,can lead to error accumulation.To address scenarios with incomplete demonstrations,we propose a robust imitation learning method based on generative trajectory modeling.Our approach utilizes a Decision Transformer to generate continuous state-action sequences conditioned on incomplete demonstrations,thereby completing the trajectories.The quality of these generated trajectories is controlled by setting a target return.The completed trajectories are then fed into a diffusion model for further trajectory generation.Additionally,we introduce a novel scoring mechanism to evaluate whether the trajectories generated by the diffusion model conform to the environmental dynamics constraints present in the expert demonstrations.This model can generate high-reward trajectories that adhere to the dynamics constraints while effectively completing imperfect trajectories,thereby significantly reducing error accumulation and improving planning accuracy.Experiments show that our method outperforms existing offline reinforcement learning approaches.It successfully addresses the planning-to-reality mismatch problem in high-dimensional continuous spaces and demonstrates superior robustness and generalization capabilities.