Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration, as highly expressive movements often incur unnecessary time and energy costs. In collaborative environments, legibility allows a human observer a better understanding of the robot's actions, increasing safety and trust. However, these behaviors result in sub-optimal and exaggerated trajectories that are redundant in low-ambiguity scenarios where the robot's goal is already obvious. To address this trade-off, we propose Style-Conditioned Diffusion Policy (SCDP), a modular framework that constrains the trajectory generation of a pre-trained diffusion model toward either legibility or efficiency based on the environment's configuration. Our method utilizes a post-training pipeline that freezes the base policy and trains a lightweight scene encoder and conditioning predictor to modulate the diffusion process. At inference time, an ambiguity detection module activates the appropriate conditioning, prioritizing expressive motion only for ambiguous goals and reverting to efficient paths otherwise. We evaluate SCDP on manipulation and navigation tasks, and results show that it enhances legibility in ambiguous settings while preserving optimal efficiency when legibility is unnecessary, all without retraining the base policy.

翻译：在人与机器人协作中，如何在效率与动作透明性之间取得平衡是一个核心挑战，因为高度表现力的运动往往会带来不必要的时间与能量消耗。在协作环境中，可读性使人类观察者能更好地理解机器人的行为，从而提升安全性与信任度。然而，这类行为会导致轨迹次优且夸张，在机器人目标已十分明确的低模糊度场景中显得冗余。为解决这一权衡问题，我们提出风格条件扩散策略（SCDP），这是一种模块化框架，能够根据环境配置约束预训练扩散模型的轨迹生成方向，使其侧重于可读性或效率。我们的方法采用后训练流程：冻结基础策略，并训练轻量级场景编码器与条件预测器以调节扩散过程。在推理阶段，模糊度检测模块会激活相应的条件机制——仅在目标模糊时优先采用表现性运动，其余情况则恢复为高效路径。我们在操作与导航任务上对SCDP进行评估，结果表明：该方法在模糊场景中有效增强了可读性，同时在无需可读性时保持最优效率，且无需重新训练基础策略。