让机器人运动的意图表达可调节，从清晰到模糊自由切换。
Controlling Intent Expressiveness in Robot Motion with Diffusion Models
- 用信息势场模型给轨迹打连续可调的清晰度分数
- 两阶段扩散模型生成不同清晰度的运动路径并转为可执行动作
- 在2D/3D任务中实现多样且可控的意图表达，性能媲美顶尖方法
机器人运动的可读性在人机交互中至关重要，能让人类快速理解机器人的意图目标。传统轨迹生成方法虽注重效率，却常使意图不清晰。现有可读性方法通常仅生成单一“最清晰”轨迹，无法根据情境调节意图表达程度。本文提出一种新型运动生成框架，实现从高度清晰到高度模糊的全谱可调控可读性。引入基于信息势场的建模方法，为轨迹分配连续可调的可读性评分，并构建两阶段扩散框架：先生成指定可读性水平的路径，再将其转化为可执行的机器人动作。2D与3D抓取任务实验表明，该方法能生成多样化、可控的运动，且性能达到当前最优水平。
原文摘要 · Abstract (English)
Legibility of robot motion is critical in human-robot interaction, as it allows humans to quickly infer a robot's intended goal. Although traditional trajectory generation methods typically prioritize efficiency, they often fail to make the robot's intentions clear to humans. Meanwhile, existing approaches to legible motion usually produce only a single "most legible" trajectory, overlooking the need to modulate intent expressiveness in different contexts. In this work, we propose a novel motion generation framework that enables controllable legibility across the full spectrum, from highly legible to highly ambiguous motions. We introduce a modeling approach based on an Information Potential Field to assign continuous legibility scores to trajectories, and build upon it with a two-stage diffusion framework that first generates paths at specified legibility levels and then translates them into executable robot actions. Experiments in both 2D and 3D reaching tasks demonstrate that our approach produces diverse and controllable motions with varying degrees of legibility, while achieving performance comparable to SOTA. Code and project page: https://legibility-modulator.github.io.
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