让机器人根据语言指令实时调整动作，实现动态适应。
Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
- 通过分解示范序列并弱监督标注，学习指令到动作的映射关系。
- 在擦拭和抓取放置任务中实现执行过程中的在线动作调整。
- 适合需要灵活响应语言指令的机器人交互场景。
在机器人学习领域，通过语言指令协调机器人动作正变得越来越可行。然而，将动作适配于人类指令仍具挑战性，因为此类指令通常为定性描述，需探索满足不同条件的行为。本文提出一种运动生成模型，可在任务执行过程中根据修饰类指令动态调整机器人动作。该方法通过将示范数据分割为短序列，并为特定修饰类型分配弱监督标签，学习从修饰指令到动作的映射。我们在擦拭和抓取放置任务中评估了该方法，结果表明其可在线响应修饰指令，而传统批处理方法无法在执行过程中进行适应。
原文摘要 · Abstract (English)
In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains challenging, as such instructions are often qualitative and require exploring behaviors that satisfy varying conditions. This paper proposes a motion generation model that adapts robot actions in response to modifier directives human instructions imposing behavioral conditions during task execution. The proposed method learns a mapping from modifier directives to actions by segmenting demonstrations into short sequences, assigning weakly supervised labels corresponding to specific modifier types. We evaluated our method in wiping and pick and place tasks. Results show that it can adjust motions online in response to modifier directives, unlike conventional batch-based methods that cannot adapt during execution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。