从示范中主动学习高维未知约束，提升推理精度
Active Constraint Learning in High Dimensions from Demonstrations
- 用高斯过程迭代建模未知约束，主动选择关键起终点
- 在稀疏示范下仍能准确推断约束，优于随机采样基线
- 适用于高维非线性系统，适合机器人运动规划场景
我们提出一种迭代主动约束学习（ACL）算法，基于示范学习（LfD）范式，智能地请求有信息量的示范轨迹，以推断演示者环境中未知的约束。该方法在已有示范数据集上迭代训练高斯过程（GP）以表示未知约束，利用得到的GP后验分布查询起始/目标状态，并生成具有信息量的示范加入数据集。在高维非线性动力学和未知非线性约束的仿真与硬件实验中，我们的方法相比基于随机采样的基线，在逐步生成的稀疏但有信息量的示范下，显著提升了约束推断的准确性。
原文摘要 · Abstract (English)
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.
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