When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynamics and task objective, and the maximum likelihood constraints can be identified. Prior constraint inference work has focused mainly on deterministic models. Stochastic models, however, can capture the uncertainty and risk tolerance that are often present in real systems of interest. This paper extends maximum likelihood constraint inference to stochastic applications by using maximum causal entropy likelihoods. Furthermore, we propose an efficient algorithm that computes constraint likelihood and risk tolerance in a unified Bellman backup, allowing us to generalize to stochastic systems without increasing computational complexity.

中文翻译：

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随机论证的最大似然约束推论

当专家操作危险的动态系统时，理想的约束信息将默认包含在他们演示的轨迹和控制中。给定系统动力学和任务目标，可以计算出这些演示的可能性，并且可以确定最大可能性约束。先验约束推理工作主要集中在确定性模型上。但是，随机模型可以捕获实际感兴趣的系统中经常存在的不确定性和风险承受能力。本文通过使用最大因果熵似然将最大似然约束推论扩展到随机应用中。此外，我们提出了一种有效的算法，可以在统一的Bellman备份中计算约束可能性和风险承受能力，