Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human–robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable models. An advantage of structuring generative models in this way is that we can extract causal relationships between symbols that might form part of the user’s specification of the task, as manifested in the demonstrations. We further parameterize these specifications through constraint optimization in order to find a safety envelope under which motion planning can be performed. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8–99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task.

中文翻译：

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从演示到任务空间规范。使用因果分析从演示中提取规则参数化

学习用户行为模型是一个重要问题，广泛适用于许多需要人机交互的应用程序领域。在这项工作中，我们表明可以通过在潜在空间内强制执行首选任务解决方案的聚类来学习从人类演示中提取的不同用户行为类型的生成模型。我们使用这些模型来区分用户类型并查找具有重叠解决方案的案例。此外，我们可以通过在学习的可微模型中进行反向传播，来更改最初猜测的解决方案，以满足构成特定用户类型的偏好。以这种方式构造生成模型的一个优势是，我们可以提取符号之间的因果关系，这些因果关系可能构成用户任务说明的一部分，如示威游行所示。我们通过约束优化进一步对这些规格进行参数化，以便找到可以执行运动计划的安全范围。我们表明，提出的方法能够正确区分三种用户类型，他们的动作谨慎程度不同，同时在桌面环境中执行由运动觉察驱动的机器人移动对象的任务。我们的方法在指定时间内成功识别了99％[97.8–99.8]情况下的正确类型，该类型优于IRL基线。我们还表明，我们提出的方法可以正确地将默认轨迹更改为满足特定用户规范的默认轨迹，即使其中包含看不见的对象。