机器人能实时察觉人类目标变化并主动调整协作策略。
I've Changed My Mind: Robots Adapting to Changing Human Goals during Collaboration
- 通过追踪多个动作序列并验证其合理性来检测目标变更。
- 目标切换后快速收敛，任务完成时间减少20%以上。
- 适合需要动态适应的人机协作场景，如厨房助手机器人。
为实现高效人机协作，机器人需在任务过程中持续匹配人类目标，即使目标发生改变。以往方法多假设目标固定，仅进行一次性的目标推断，但在真实场景中，人类常中途改变目标，导致机器人难以适应。本文提出一种通过追踪多个候选动作序列并结合策略库验证其合理性来检测目标变化的方法。一旦检测到变更，机器人将重新评估相关历史动作，并构建滚动规划树（RHP），主动选择能协助人类且鼓励其执行差异性动作的策略以揭示新目标。我们在包含最多30种不同食谱的协作烹饪环境中评估该方法，与三种现有目标预测算法对比，结果表明本方法在目标切换后能更快收敛至正确目标，显著缩短任务完成时间，提升协作效率。
原文摘要 · Abstract (English)
For effective human-robot collaboration, a robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world scenarios, humans frequently shift goals, making it challenging for robots to adapt without explicit communication. We propose a method for detecting goal changes by tracking multiple candidate action sequences and verifying their plausibility against a policy bank. Upon detecting a change, the robot refines its belief in relevant past actions and constructs Receding Horizon Planning (RHP) trees to actively select actions that assist the human while encouraging Differentiating Actions to reveal their updated goal. We evaluate our approach in a collaborative cooking environment with up to 30 unique recipes and compare it to three comparable human goal prediction algorithms. Our method outperforms all baselines, quickly converging to the correct goal after a switch, reducing task completion time, and improving collaboration efficiency.
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