基于边界遵循偏好模型的用户演示贝叶斯约束推断
Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models
AI总结：
针对机器人约束推断计算成本高、不稳定的问题，提出基于演示偏好的贝叶斯约束推断方法，无需迭代更新策略、采用演示组排序、适配不同约束违反程度，准确率优于现有方法。
摘要：
对机器人而言，感知约束的存在对于获得安全策略至关重要。然而，明确指定环境中的所有约束可能是一项具有挑战性的任务。当前最先进的约束推断算法从演示中学习约束，但往往计算成本高昂且容易出现不稳定问题。本文提出了一种新颖的贝叶斯方法，该方法基于对演示的偏好来推断约束。我们所提方法的主要优势在于：1）无需在每次迭代中计算新策略即可推断约束；2）采用简单且更符合实际的演示组排序方式，无需对所有演示进行两两比较；3）能够适应约束违反程度各不相同的情况。我们的实验结果表明，与当前最先进的约束推断方法相比，所提出的贝叶斯方法能够更准确地推断出不同严重程度的约束。
英文摘要：
It is crucial for robots to be aware of the presence of constraints in order to acquire safe policies. However, explicitly specifying all constraints in an environment can be a challenging task. State-of-the-art constraint inference algorithms learn constraints from demonstrations, but tend to be computationally expensive and prone to instability issues. In this paper, we propose a novel Bayesian method that infers constraints based on preferences over demonstrations. The main advantages of our proposed approach are that it 1) infers constraints without calculating a new policy at each iteration, 2) uses a simple and more realistic ranking of groups of demonstrations, without requiring pairwise comparisons over all demonstrations, and 3) adapts to cases where there are varying levels of constraint violation. Our empirical results demonstrate that our proposed Bayesian approach infers constraints of varying severity, more accurately than state-of-the-art constraint inference methods.
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。
