FoAM：面向机器人操作的前瞻增强多任务模仿策略
FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation
AI总结：
FoAM通过引入多模态目标条件和前瞻增强，提升多任务模仿学习在机器人操作中的动作可靠性与泛化能力，在超100个任务中成功率最高提升41%。
摘要：
多任务模仿学习（MTIL）在机器人操作领域展现出巨大潜力，它使智能体能够使用单一策略执行多种任务。这简化了策略部署，并增强了智能体在不同场景中的适应性。然而，仍存在关键挑战，例如保持动作可靠性（例如，避免偏离标称任务轨迹的异常动作序列）以及通过少量专家演示泛化到未见过的任务。为解决这些挑战，我们提出了前瞻增强操作策略（FoAM），这是一种新颖的MTIL策略，首创使用多模态目标条件作为输入，并在一般动作重建之外引入了前瞻增强。FoAM使智能体能够推理其动作的视觉后果（状态），并学习捕捉细微任务变化的更具表达力的嵌入。在仿真和真实世界环境中超过100个任务的广泛实验表明，FoAM显著提升了MTIL策略的性能，在成功率上比最先进的基线高出最多41%。同时，我们发布了仿真套件，包括共10个场景和超过80个为操作策略训练和评估设计的挑战性任务。项目详情请见项目主页projFoAM.github.io。
英文摘要：
Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a single policy. This simplifies the policy deployment and enhances the agent's adaptability across different scenarios. However, key challenges remain, such as maintaining action reliability (e.g., avoiding abnormal action sequences that deviate from nominal task trajectories) and generalizing to unseen tasks with a few expert demonstrations. To address these challenges, we introduce the Foresight-Augmented Manipulation Policy (FoAM), a novel MTIL policy that pioneers the use of multi-modal goal condition as input and introduces a foresight augmentation in addition to the general action reconstruction. FoAM enables the agent to reason about the visual consequences (states) of its actions and learn more expressive embedding that captures nuanced task variations. Extensive experiments on over 100 tasks in simulation and real-world settings demonstrate that FoAM significantly enhances MTIL policy performance, outperforming state-of-the-art baselines by up to 41% in success rate. Meanwhile, we released our simulation suites, including a total of 10 scenarios and over 80 challenging tasks designed for manipulation policy training and evaluation. See the project homepage projFoAM.github.io for project details.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
