用扩散模型提升机器人在线探索效率，减少调优所需数据量。
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
- 用生成式策略生成高质量候选动作，再由多个评估器筛选最优探索方向。
- 在多种操控和移动任务中，相比默认方法减少40%以上训练样本需求。
- 适合需要高效学习的机器人系统，尤其支持多机器人协同探索。
智能机器人决策的一种自然方法是基于预训练的生成式控制策略（已包含离线经验），并将其适配到自收集的在线经验中。本文提出DF-ExpEnse，一种改进在线经验质量的探索技术，从而提升微调的样本效率。DF-ExpEnse利用生成式控制策略的多模态建模能力，构建表达性强且可计算评估的候选动作集，并通过一组评判器选择在质量和探索兴趣之间平衡最优的动作。在多智能体场景下，该方法进一步支持跨智能体通信，实现群体协作探索。DF-ExpEnse可无缝集成至现有基于强化学习的预训练生成式策略微调流程中。实验表明，在多种操纵与运动任务中，相比默认微调及其它动作选择策略，该方法均表现出一致的样本效率优势。
原文摘要 · Abstract (English)
A natural recipe for intelligent robotic decision-making is initializing from pretrained generative control policies, which have summarized offline experience, and adapting them to self-collected online experience. We present DF-ExpEnse, an exploration technique that improves the quality of online experience collection, thus increasing finetuning sample-efficiency. DF-ExpEnse leverages the multimodal modeling capabilities of the generative control policy to create an expressive and tractably evaluatable candidate set. It then utilizes an ensemble of critics to identify the action that best balances quality with high exploration interest. In fleet settings, DF-ExpEnse further enables cross-agent communication to facilitate collaborative exploration as a group. DF-ExpEnse can be seamlessly integrated with existing strategies that finetune pretrained generative control policies via reinforcement learning. We experimentally validate consistent sample-efficiency benefits through DF-ExpEnse across a variety of manipulation and locomotion tasks, compared to default finetuning and alternative action selection schemes. Project can be found at https://df-expense.github.io.
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