Solving long sequential tasks poses a significant challenge in embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is an active area of research. In this work, we present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN), to address the challenge of solving multiple complex tasks over long time horizons in robotic manipulation. ROMAN achieves task versatility and robust failure recovery by integrating behavioural cloning, imitation learning, and reinforcement learning. It consists of a central manipulation network that coordinates an ensemble of various neural networks, each specialising in distinct re-combinable sub-tasks to generate their correct in-sequence actions for solving complex long-horizon manipulation tasks. Experimental results show that by orchestrating and activating these specialised manipulation experts, ROMAN generates correct sequential activations for accomplishing long sequences of sophisticated manipulation tasks and achieving adaptive behaviours beyond demonstrations, while exhibiting robustness to various sensory noises. These results demonstrate the significance and versatility of ROMAN's dynamic adaptability featuring autonomous failure recovery capabilities, and highlight its potential for various autonomous manipulation tasks that demand adaptive motor skills.

翻译：在具身人工智能领域，解决长序列任务始终是一项重大挑战。赋予机器人系统广泛的操作技能以执行多样化的序列任务，是当前研究的热点方向。本文提出一种混合分层学习框架——机器人操作网络（ROMAN），旨在解决机器人操作中长时域多复杂任务的执行难题。该框架通过整合行为克隆、模仿学习与强化学习，实现了任务通用性与鲁棒故障恢复能力。ROMAN的核心是一个中央操作网络，用于协调由多个神经网络构成的集成系统——每个网络专门处理可重组的不同子任务，生成正确的序列化动作以解决复杂长时域操作任务。实验结果表明，通过编排与激活这些专业化操作专家模块，ROMAN可生成正确的序列化激活指令，完成复杂的多步操作任务，并展现出超越演示数据的自适应行为，同时具备应对多类感知噪声的鲁棒性。这些成果验证了ROMAN动态自适应能力（含自主故障恢复机制）的重要价值与通用性，突显其在需要适应性运动技能的自主操作任务中的广阔应用前景。