Learning to predict trajectories of cooperatively navigating agents
Learning to predict trajectories of cooperatively navigating agents
复制标题
学习预测协作导航代理的轨迹
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Wolfram Burgard
中科院分区：
文献类型：
--
作者：
Henrik Kretzschmar;M. Kuderer;Wolfram Burgard
对多个相互作用的智能体的导航行为进行建模的问题出现在包括机器人技术、计算机图形学和行为科学在内的不同领域。在本文中，我们提出了一种从演示中学习相互作用的智能体的复合导航行为的方法。最终导致观察到的智能体连续轨迹的决策过程通常也包含离散决策，这些离散决策将复合轨迹的空间划分为同伦类。因此，我们的方法使用一种混合概率分布，它由同伦类上的离散分布以及每个同伦类的复合轨迹上的连续分布组成。我们的方法学习这种分布的模型参数，这些参数在期望上与根据用户定义的特征所观察到的行为相匹配。为了计算高维连续分布上的特征期望，我们使用哈密顿马尔可夫链蒙特卡罗采样。我们利用由于物理约束而使分布具有高度结构化的特点，并将采样过程引导到高概率区域。我们将我们的方法应用于学习行人的行为，并证明它优于最先进的方法。
The problem of modeling the navigation behavior of multiple interacting agents arises in different areas including robotics, computer graphics, and behavioral science. In this paper, we present an approach to learn the composite navigation behavior of interacting agents from demonstrations. The decision process that ultimately leads to the observed continuous trajectories of the agents often also comprises discrete decisions, which partition the space of composite trajectories into homotopy classes. Therefore, our method uses a mixture probability distribution that consists of a discrete distribution over the homotopy classes and continuous distributions over the composite trajectories for each homotopy class. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. We exploit that the distributions are highly structured due to physical constraints and guide the sampling process to regions of high probability. We apply our approach to learning the behavior of pedestrians and demonstrate that it outperforms state-of-the-art methods.