Abstract
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.
Abstract (translated)
在机器人学习领域，通过语言指令协调机器人的行动正变得越来越可行。然而，根据人类的指示调整动作仍然是一项挑战，因为这些指示往往是定性的，并且需要探索能够满足各种条件的行为。本文提出了一种运动生成模型，该模型能够在任务执行过程中根据修饰性的人类指令来适应和调整机器人的行为。所提出的方法通过将演示分割成短序列，并分配与特定修饰类型对应的弱监督标签，学习了从修饰性指示到动作的映射。 我们在擦拭和取物放置等任务中评估了我们的方法。实验结果表明，该模型能够在线响应修饰指令进行运动调整，而传统的基于批次的方法则无法在执行过程中进行适应。
URL
https://arxiv.org/abs/2509.04737
