永不终止的行为克隆代理用于机器人操作
Never-Ending Behavior-Cloning Agent for Robotic Manipulation
AI总结：
本文提出NBAgent，通过持续学习技能共享和特定属性，解决具身机器人中3D场景表示和人类水平任务学习的挑战。
摘要：
依赖多模态观测，具身机器人（例如人形机器人）可以在无结构的现实世界环境中执行多种机器人操作任务。然而，大多数语言条件的行为克隆代理在机器人中仍然面临长期存在的挑战，即3D场景表示和人类水平的任务学习，当适应到一系列新任务的实践中。我们在此通过NBAgent在具身机器人中研究这些挑战，这是一种开创性的语言条件永不终止行为克隆代理，可以持续从技能共享和技能特定属性中学习新型3D场景语义和机器人操作技能。具体而言，我们提出了一种技能共享语义渲染模块和一种技能共享表示蒸馏模块，以有效从技能共享属性中学习3D场景语义，进一步解决3D场景表示的遗漏问题。同时，我们建立了技能特定的进化计划器，以执行操作知识解耦，它可以持续嵌入如人类一样的新技能特定知识。最后，我们设计了一个永不终止的具身机器人操作基准，昂贵的实验表明了我们方法的显著性能。
英文摘要：
Relying on multi-modal observations, embodied robots (e.g., humanoid robots) could perform multiple robotic manipulation tasks in unstructured real-world environments. However, most language-conditioned behavior-cloning agents in robots still face existing long-standing challenges, i.e., 3D scene representation and human-level task learning, when adapting into a series of new tasks in practical scenarios. We here investigate these above challenges with NBAgent in embodied robots, a pioneering language-conditioned Never-ending Behavior-cloning Agent, which can continually learn observation knowledge of novel 3D scene semantics and robot manipulation skills from skill-shared and skill-specific attributes, respectively. Specifically, we propose a skill-shared semantic rendering module and a skill-shared representation distillation module to effectively learn 3D scene semantics from skill-shared attribute, further tackling 3D scene representation overlooking. Meanwhile, we establish a skill-specific evolving planner to perform manipulation knowledge decoupling, which can continually embed novel skill-specific knowledge like human from latent and low-rank space. Finally, we design a never-ending embodied robot manipulation benchmark, and expensive experiments demonstrate the significant performance of our method.
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。
