RILI：强有力地影响潜在意图
RILI: Robustly Influencing Latent Intent
摘要：
当机器人与人类伙伴互动时，这些伙伴通常会改变他们的行为以回应机器人。一方面，这是具有挑战性的，因为机器人必须学会与动态伙伴协调。但另一方面，如果机器人理解这些动态，它可以利用自己的行为，影响人类，并引导团队进行有效的合作。先前的研究使机器人能够学习影响其他机器人或模拟代理。在本文中，我们将这些学习方法扩展到现在影响人类。让人类特别难以影响的是--不仅人类对机器人做出反应--而且单个用户对机器人的反应可能会随着时间的推移而改变，不同的人类会以不同的方式对相同的机器人行为做出反应。因此，我们提出了一个强大的方法，学会影响不断变化的合作伙伴动态。我们的方法首先在重复的交互中与一组合作伙伴一起训练，并根据之前的状态，动作和奖励来学习预测当前合作伙伴的行为。接下来，我们通过采样机器人与原始合作伙伴一起学习的轨迹来快速适应新的合作伙伴，然后利用这些现有的行为来影响新的合作伙伴动态。我们将所得到的算法与模拟环境中最先进的基线进行比较，并对机器人和参与者合作建造塔楼的用户进行研究。我们发现，我们的方法优于替代方案，即使合作伙伴遵循新的或意想不到的动态。用户研究的视频可以在这里获得：https://youtu.be/lYsWM8An18g
英文摘要：
When robots interact with human partners, often these partners change their behavior in response to the robot. On the one hand this is challenging because the robot must learn to coordinate with a dynamic partner. But on the other hand -- if the robot understands these dynamics -- it can harness its own behavior, influence the human, and guide the team towards effective collaboration. Prior research enables robots to learn to influence other robots or simulated agents. In this paper we extend these learning approaches to now influence humans. What makes humans especially hard to influence is that -- not only do humans react to the robot -- but the way a single user reacts to the robot may change over time, and different humans will respond to the same robot behavior in different ways. We therefore propose a robust approach that learns to influence changing partner dynamics. Our method first trains with a set of partners across repeated interactions, and learns to predict the current partner's behavior based on the previous states, actions, and rewards. Next, we rapidly adapt to new partners by sampling trajectories the robot learned with the original partners, and then leveraging those existing behaviors to influence the new partner dynamics. We compare our resulting algorithm to state-of-the-art baselines across simulated environments and a user study where the robot and participants collaborate to build towers. We find that our approach outperforms the alternatives, even when the partner follows new or unexpected dynamics. Videos of the user study are available here: https://youtu.be/lYsWM8An18g
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
