We present an iterative active constraint learning (ACL) algorithm, within the learning from demonstrations (LfD) paradigm, which intelligently solicits informative demonstration trajectories for inferring an unknown constraint in the demonstrator's environment. Our approach iteratively trains a Gaussian process (GP) on the available demonstration dataset to represent the unknown constraints, uses the resulting GP posterior to query start/goal states, and generates informative demonstrations which are added to the dataset. Across simulation and hardware experiments using high-dimensional nonlinear dynamics and unknown nonlinear constraints, our method outperforms a baseline, random-sampling based method at accurately performing constraint inference from an iteratively generated set of sparse but informative demonstrations.

翻译：我们提出了一种在演示学习范式下的迭代式主动约束学习算法，该算法能够智能地获取信息丰富的演示轨迹，以推断演示者环境中的未知约束。我们的方法在现有演示数据集上迭代训练高斯过程来表示未知约束，利用所得的高斯过程后验查询起始/目标状态，并生成信息丰富的演示样本加入数据集。通过在高维非线性动力学和未知非线性约束下的仿真与硬件实验验证，本方法在基于迭代生成的稀疏但信息丰富的演示集进行精确约束推断方面，显著优于基于随机采样的基线方法。